Systems and methods for fault detection and control in an electric aircraft

ABSTRACT

A system for fault detection and control in an electric aircraft including an inertial measurement unit, the inertial measurement unit including at least a sensor configured to detect a torque datum associated with at least a propulsor. The system includes an observer, the observer configured to generate a torque prediction datum associated with the at least a propulsor, compare the torque prediction datum with the torque datum, and generate a residual datum as a function of the comparison. The system includes a mixer, the mixer comprising circuitry configured to generate, as a function of the residual datum, a torque priority command datum and transmit, to the at least a propulsor, the torque priority command datum.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of Non-Provisional Application No.17/855,651 filed on Jun. 30, 2022 and entitled “SYSTEMS AND METHODS FORFAULT DETECTION AND CONTROL IN AN ELECTRIC AIRCRAFT,” which itselfclaims priority to Non-Provisional Application No. 17/365,049 filed onJul. 1, 2021 and entitled “SYSTEMS AND METHODS FOR FAULT DETECTION ANDCONTROL IN AN ELECTRIC AIRCRAFT” the entirety of both of which isincorporated herein by reference.

FIELD OF THE INVENTION

The present invention generally relates to the field of electricaircraft. In particular, the present invention is directed to systemsand methods for fault detection and control under one rotor inoperablecondition configured for use in an electric aircraft.

BACKGROUND

In electrically propelled vehicles, such as an electric vertical takeoffand landing (eVTOL) aircraft, it is essential to maintain the integrityof the aircraft until safe landing. In some flights, a component of theaircraft may experience a malfunction or failure which will put theaircraft in an unsafe mode which will compromise the safety of theaircraft, passengers and onboard cargo. A system and method for faultdetection and control therefor configured for use in an electricaircraft is useful and necessary to control aircraft under certainconditions, in embodiments.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for fault detection and control in an electricaircraft, the system including a flight controller, the flightcontroller including at least a sensor, wherein the at least a sensorconfigured to detect a sensed datum associated with at least apropulsor. The system further including an observer, the observerconfigured to generate a prediction datum associated with the at least apropulsor, compare the prediction datum with the sensed datum, generatea residual datum as a function of the comparison. and determine afailure state of the at least a propulsor as a function of the residualdatum. The system further including a mixer configured to operate in afirst mode in which the mixer is configured to control a first pluralityof the at least a propulsor and a second mode in which the mixer isconfigured to control a second plurality of the at least a propulsor,the mixer comprising circuitry configured to generate, as a function ofthe residual datum and the failure state, a torque priority commanddatum and transmit, to the at least a propulsor, the torque prioritycommand datum configured to command operation of the first plurality ofthe at least a propulsor.

In another aspect, a method for fault detection and control in anelectric aircraft, the method including detecting, at an at least asensor, a sensed datum associated with at least a propulsor, generating,at an observer, a prediction datum associated with the at least apropulsor comparing, at the observer, the prediction datum and thesensed datum, generating, at the observer, as a function of thecomparison, a residual datum. generating, at the observer, as a functionof the residual datum, a failure state of the at least a propulsor,generating, at a mixer that is configured to operate in a first mode inwhich the mixer is configured to control a first plurality of the atleast a propulsor and a second mode in which the mixer is configured tocontrol a second plurality of the at least a propulsor, as a function ofthe residual datum and the failure state, a torque priority commanddatum, and transmitting, to the at least a propulsor, the torquepriority command datum configured to command operation of at least oneflight component of the electric aircraft.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram of an exemplary embodiment of a system forfault detection and control under one rotor inoperable conditions;

FIG. 2 is a block diagram of an exemplary embodiment of an observer;

FIG. 3 is a block diagram of an exemplary embodiment of a flightcontroller;

FIG. 4 is a flow diagram of an exemplary method for fault detection andcontrol under one rotor inoperable conditions;

FIG. 5 is an illustration of an exemplary embodiment of an aircraft inisometric view;

FIG. 6 is a block diagram of an exemplary embodiment of a machinelearning module;

FIG. 7 is a block diagram of an exemplary embodiment of a propulsionsystem of an electric aircraft in accordance with one or moreembodiments of the present disclosure; and

FIG. 8 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. As used herein, the word “exemplary” or “illustrative” means“serving as an example, instance, or illustration.” Any implementationdescribed herein as “exemplary” or “illustrative” is not necessarily tobe construed as preferred or advantageous over other implementations.All of the implementations described below are exemplary implementationsprovided to enable persons skilled in the art to make or use theembodiments of the disclosure and are not intended to limit the scope ofthe disclosure, which is defined by the claims. For purposes ofdescription herein, the terms “upper”, “lower”, “left”, “rear”, “right”,“front”, “vertical”, “horizontal”, and derivatives thereof shall relateto orientations as illustrated for exemplary purposes in FIG. 5 .Furthermore, there is no intention to be bound by any expressed orimplied theory presented in the preceding technical field, background,brief summary or the following detailed description. It is also to beunderstood that the specific devices and processes illustrated in theattached drawings, and described in the following specification, aresimply embodiments of the inventive concepts defined in the appendedclaims. Hence, specific dimensions and other physical characteristicsrelating to the embodiments disclosed herein are not to be considered aslimiting, unless the claims expressly state otherwise.

Referring now to FIG. 1 , an exemplary embodiment of a system 100 forfault detection and control under one rotor inoperable conditionconfigured for use in an electric aircraft is illustrated. Systemincludes at least a computing device, which may include or be aprocessor, flight controller, and/or controller, or portions thereof.Computing device may include any computing device as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Computing device may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Computing device may include a single computingdevice operating independently, or may include two or more computingdevice operating in concert, in parallel, sequentially or the like; twoor more computing devices may be included together in a single computingdevice or in two or more computing devices. Computing device mayinterface or communicate with one or more additional devices asdescribed below in further detail via a network interface device.Network interface device may be utilized for connecting computing deviceto one or more of a variety of networks, and one or more devices.Examples of a network interface device include, but are not limited to,a network interface card (e.g., a mobile network interface card, a LANcard), a modem, and any combination thereof. Examples of a networkinclude, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device.Computing device may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. Computing device may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. Computing device may distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. Computing device may be implementedusing a “shared nothing” architecture in which data is cached at theworker, in an embodiment, this may enable scalability of system 100and/or computing device.

With continued reference to FIG. 1 , computing device may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, computing devicemay be configured to perform a single step or sequence repeatedly untila desired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Computing device mayperform any step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

With continued reference to FIG. 1 , system 100 for fault detection andcontrol under one rotor inoperable condition configured for use in anelectric aircraft includes a flight controller 104. Flight controller104 may be consistent with any flight controller or controller asdescribed herein such as computing device herein above and/or flightcontroller 304 herein below. Still referring to FIG. 1 , system 100configured for use in electric aircraft is presented. System 100includes flight controller 104 configured to provide signals to anypropulsor including at least a propulsor as described herein below.Flight controller 104 may be a computing device as previously disclosed.Flight controller 104 may be a processor configured to control theoutput of a plurality of propulsors in response to inputs. Inputs tothis system may include pilot manipulations of physical controlinterfaces, remote signals generated from electronic devices, voicecommands, physiological readings like eye movements, pedal manipulation,or a combination thereof, to name a few. Flight controller 104 mayinclude a proportional-integral-derivative (PID) controller. A “PIDcontroller”, for the purposes of this disclosure, is a control loopmechanism employing feedback that calculates an error value as thedifference between a desired setpoint and a measured process variableand applies a correction based on proportional, integral, and derivativeterms; integral and derivative terms may be generated, respectively,using analog integrators and differentiators constructed withoperational amplifiers and/or digital integrators and differentiators,as a non-limiting example. PID controllers may automatically applyaccurate and responsive correction to a control function in a loop, suchthat over time the correction remains responsive to the previous outputand actively controls an output. Flight controller 104 may includedamping, including critical damping to attain the desired setpoint,which may be an output to a propulsor in a timely and accurate way.

Still referring to FIG. 1 , flight controller 104 may be implementedconsistently with any flight controller as described herein. Flightcontroller 104 is configured to provide signals comprising a pluralityof attitude commands. “Attitude”, for the purposes of this disclosure,is the relative orientation of a body, in this case an electricaircraft, as compared to earth’s surface or any other reference pointand/or coordinate system. Attitude may be displayed to pilots,personnel, remote users, or one or more computing devices in an attitudeindicator, such as without limitation a visual representation of thehorizon and its relative orientation to the aircraft. Signals mayinclude a desired change in aircraft trajectory as inputted by anonboard or offboard pilot, remotely located user, one or more computingdevices such as an “autopilot” program or module, any combinationthereof, or the like. Initial vehicle torque signal 108 may includewithout limitation one or more electrical signals, audiovisual signals,physical indications of desired vehicle-level torques and forces, or thelike. “Trajectory”, for the purposes of this disclosure is the pathfollowed by a projectile or vehicle flying or an object moving under theaction of given forces. Trajectory may be altered by aircraft controlsurfaces and/or one or more propulsors working in tandem to manipulate afluid medium in which the object is moving through. Initial vehicletorque signal may include a signal generated from manipulation of apilot input control consistent with the entirety of this disclosure.

Further referring to FIG. 1 , flight controller 104 may include one ormore circuit elements communicatively connected together. One or moresensors may be communicatively connected to at least a pilot control,the manipulation of which, may constitute at least an aircraft command.Signals may include electrical, electromagnetic, visual, audio, radiowaves, or another undisclosed signal type alone or in combination.Signals may be configured to translate and/or encode a pilot’s desireand/or instruction to manipulate one or more elements of an aircraft’soperation and communicate said desires to one or more other elementscommunicatively connected thereto. Signals may be electrical signalstransmitted through wires, busses, wirelessly, or otherwise electricallyconductive paths transmitted from one component to at least a secondcomponent. For example, and without limitation, a pilot may desire toincrease torque output in at least a propulsor consistent with thisdisclosure and manipulate an input to indicate as such. Signals may begenerated from manipulation and transmitted to a flight controller,controller, processor, propulsor, flight component, or other componentsand/or combinations thereof to command at least a propulsor to increasetorque output. At least a sensor communicatively connected to at least apilot control may include a sensor disposed on, near, around or withinat least pilot control. At least a sensor may include a motion sensor.“Motion sensor”, for the purposes of this disclosure refers to a deviceor component configured to detect physical movement of an object orgrouping of objects. One of ordinary skill in the art would appreciate,after reviewing the entirety of this disclosure, that motion may includea plurality of types including but not limited to: spinning, rotating,oscillating, gyrating, jumping, sliding, reciprocating, or the like. Atleast a sensor may include, torque sensor, gyroscope, accelerometer,torque sensor, magnetometer, inertial measurement unit (IMU), pressuresensor, force sensor, proximity sensor, displacement sensor, vibrationsensor, among others. At least a sensor 104 may include a sensor suitewhich may include a plurality of sensors that may detect similar orunique phenomena. For example, in a non-limiting embodiment, sensorsuite may include a plurality of accelerometers, a mixture ofaccelerometers and gyroscopes, or a mixture of an accelerometer,gyroscope, and torque sensor. The herein disclosed system and method maycomprise a plurality of sensors in the form of individual sensors or asensor suite working in tandem or individually. A sensor suite mayinclude a plurality of independent sensors, as described herein, whereany number of the described sensors may be used to detect any number ofphysical or electrical quantities associated with an aircraft powersystem or an electrical energy storage system. Independent sensors mayinclude separate sensors measuring physical or electrical quantitiesthat may be powered by and/or in communication with circuitsindependently, where each may signal sensor output to a control circuitsuch as a user graphical interface. In an embodiment, use of a pluralityof independent sensors may result in redundancy configured to employmore than one sensor that measures the same phenomenon, those sensorsbeing of the same type, a combination of, or another type of sensor notdisclosed, so that in the event one sensor fails, the ability to detectphenomenon is maintained and in a non-limiting example, a user alteraircraft usage pursuant to sensor readings.

With continued reference to FIG. 1 , a plurality of attitude commandsmay indicate one or more measurements relative to an aircraft’s pitch,roll, yaw, or throttle compared to a relative starting point. One ormore sensors may measure or detect the aircraft’s attitude and establishone or more attitude datums. An “attitude datum”, for the purposes ofthis disclosure, refers to at least an element of data identifyingand/or a pilot input or command. At least a pilot control may becommunicatively connected to any other component presented in system,the communicative connection may include redundant connectionsconfigured to safeguard against single-point failure. Attitude commandsmay indicate a pilot’s instruction to change the heading and/or trim ofan electric aircraft. Pilot input may indicate a pilot’s instruction tochange an aircraft’s pitch, roll, yaw, throttle, and/or any combinationthereof. Aircraft trajectory may be manipulated by one or more controlsurfaces and propulsors working alone or in tandem consistent with theentirety of this disclosure, hereinbelow. “Pitch”, for the purposes ofthis disclosure refers to an aircraft’s angle of attack, that is thedifference between the aircraft’s nose and a horizontal flighttrajectory. For example, an aircraft may pitch “up” when its nose isangled upward compared to horizontal flight, as in a climb maneuver. Inanother example, an aircraft may pitch “down”, when its nose is angleddownward compared to horizontal flight, like in a dive maneuver. Whenangle of attack is not an acceptable input to any system disclosedherein, proxies may be used such as pilot controls, remote controls, orsensor levels, such as true airspeed sensors, pitot tubes,pneumatic/hydraulic sensors, and the like. “Roll” for the purposes ofthis disclosure, refers to an aircraft’s position about its longitudinalaxis, that is to say that when an aircraft rotates about its axis fromits tail to its nose, and one side rolls upward, as in a bankingmaneuver. “Yaw”, for the purposes of this disclosure, refers to anaircraft’s turn angle, when an aircraft rotates about an imaginaryvertical axis intersecting the center of the earth and the fuselage ofthe aircraft. “Throttle”, for the purposes of this disclosure, refers toan aircraft outputting an amount of thrust from a propulsor. Pilotinput, when referring to throttle, may refer to a pilot’s desire toincrease or decrease thrust produced by at least a propulsor. Signalsmay include an electrical signal. At least an aircraft command mayinclude mechanical movement of any throttle consistent with the entiretyof this disclosure. Electrical signals may include analog signals,digital signals, periodic or aperiodic signal, step signals, unitimpulse signal, unit ramp signal, unit parabolic signal, signumfunction, exponential signal, rectangular signal, triangular signal,sinusoidal signal, sinc function, or pulse width modulated signal.

Flight controller 104 may be programmed to operate electronic aircraftto perform at least a flight maneuver; at least a flight maneuver mayinclude takeoff, landing, stability control maneuvers, emergencyresponse maneuvers, regulation of altitude, roll, pitch, yaw, speed,acceleration, or the like during any phase of flight. At least a flightmaneuver may include a flight plan or sequence of maneuvers to beperformed during a flight plan. Flight controller 104 may be designedand configured to operate electronic aircraft via fly-by-wire. Flightcontroller 104 is communicatively connected to each propulsor; as usedherein, flight controller 104 is communicatively connected to eachpropulsor where flight controller 104 is able to transmit signals toeach propulsor and each propulsor is configured to modify an aspect ofpropulsor behavior in response to the signals. As a non-limitingexample, flight controller 104 may transmit signals to a propulsor viaan electrical circuit connecting flight controller 104 to the propulsor;the circuit may include a direct conductive path from flight controller104 to propulsor or may include an isolated coupling such as an opticalor inductive coupling. Alternatively, or additionally, flight controller104 may communicate with a propulsor using wireless communication, suchas without limitation communication performed using electromagneticradiation including optical and/or radio communication, or communicationvia magnetic or capacitive coupling. Vehicle controller may be fullyincorporated in an electric aircraft containing a propulsor and may be aremote device operating the electric aircraft remotely via wireless orradio signals, or may be a combination thereof, such as a computingdevice in the aircraft configured to perform some steps or actionsdescribed herein while a remote device is configured to perform othersteps. Persons skilled in the art will be aware, after reviewing theentirety of this disclosure, of many different forms and protocols ofcommunication that may be used to communicatively connect flightcontroller 104 to propulsors. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various ways tomonitor resistance levels and apply resistance to linear thrust control,as used and described herein.

With continued reference to FIG. 1 , system 100 and/or flight controller104 may be configured to utilize dynamic modeling. Flight controller 104may include one or more mathematical models utilizing the same ordiffering method of control. Any one or combination of any components inthe herein disclosed system 100 may utilize differing methods of controlalone or in combination. For example, and without limitation, flightcontroller 104 may utilize differing methods of control for certainreal-world conditions, such as unusual attitude behavior, certain rangesof yaw, angle of attack, rates of movement such as rapid pitch anglechange, one propulsor (such as an electric motor or rotor) inoperable orthe like. A method of control may utilize one or more inputs specific tothe method such as yaw angle, yaw angle rate of change, or the like.“Quaternions”, for the purposes of this disclosure are mathematicalexpressions of the form α + bi + cj + dk, where i, j, and k mayrepresent unit vectors pointing along axes in three-dimensionalCartesian space. Quaternions may be used to represent rotation. A “unitquaternion” is a quaternion of unit length, i.e., a quaternion of form

$\frac{q}{\left\| q \right\|^{2}}$

where ||q|| is a norm representing a length of a quaternion q. Unitquaternions may also be called rotation quaternions as they mayrepresent a 3D rotation group as described below. In 3-dimensionalspace, according to Euler’s rotation theorem, any rotation or sequenceof rotations of a rigid body or coordinate system about a fixed pointmay be treated as equivalent to a single rotation by a given angle abouta fixed axis (called the Euler axis) that runs through the fixed point.An Euler axis may typically be represented by a unit vector u→.Therefore, any rotation in three dimensions may be represented as acombination of a vector u→ and a scalar. Quaternions may provide asimple way to encode this axis-angle representation in four numbers, andmay be used to apply the corresponding rotation to a position vector,representing a point relative to the origin in R³. Euclidean vectorssuch as (2, 3, 4) or (α_(x), α_(y), α_(z)) may be rewritten as 2i + 3j +4k or α_(x)i + α_(y)j + α_(z)k, where i, j, k are unit vectorsrepresenting the three Cartesian axes (traditionally x, y, z), and alsoobey multiplication rules of fundamental unit quaternion. Unitquaternions may represent an algebraic group of Euclidean rotations inthree dimensions in a straightforward way.

With continued reference to FIG. 1 , an aircraft quaternion control maybe a control system that uses quaternions to model motion in threedimensions, and more specifically, in the three attitude components ofaircraft orientation, pitch, roll, and yaw. Quaternions used inquaternion aircraft control may be any of the quaternions discussedherein. Quaternion control may be useful in the field of aircraftcontrol as a quaternion is a 4-dimensional vector used to describe thetransformation of a vehicle in 3-dimensions. The use of quaternions maybe favored over other descriptors due to their non-singularityproperties at any aircraft attitude. Traditional aeronautictransformations (Euler angles) may be hindered by a phenomenon known asgimbal lock. Gimbal lock may cause a loss of degree of freedom (DOF)which could lead to controller instability. Since this thesis exploresaggressive flight regimes, a quaternion attitude descriptor was chosento provide a singularity-free rotation from hover to horizontal flight.

With continued reference to FIG. 1 , system 100 includes at least asensor 108. At least a sensor 104 may be communicatively connected to atleast a pilot control, the manipulation of which, may constitute atleast an aircraft command. “Communicative connecting”, for the purposesof this disclosure, refers to two or more components electrically, orotherwise connected and configured to transmit and receive signals fromone another. Signals may include electrical, electromagnetic, visual,audio, radio waves, or another undisclosed signal type alone or incombination. At least a sensor communicatively connected to at least apilot control may include a sensor disposed on, near, around or withinat least pilot control. At least a sensor may include a motion sensor.“Motion sensor”, for the purposes of this disclosure refers to a deviceor component configured to detect physical movement of an object orgrouping of objects. One of ordinary skill in the art would appreciate,after reviewing the entirety of this disclosure, that motion may includea plurality of types including but not limited to: spinning, rotating,oscillating, gyrating, jumping, sliding, reciprocating, or the like. Atleast a sensor may include, torque sensor, gyroscope, accelerometer,torque sensor, magnetometer, inertial measurement unit (IMU), pressuresensor, force sensor, proximity sensor, displacement sensor, vibrationsensor, among others. At least a sensor 104 may include a sensor suitewhich may include a plurality of sensors that may detect similar orunique phenomena. For example, in a non-limiting embodiment, sensorsuite may include a plurality of accelerometers, a mixture ofaccelerometers and gyroscopes, or a mixture of an accelerometer,gyroscope, and torque sensor. The herein disclosed system and method maycomprise a plurality of sensors in the form of individual sensors or asensor suite working in tandem or individually. A sensor suite mayinclude a plurality of independent sensors, as described herein, whereany number of the described sensors may be used to detect any number ofphysical or electrical quantities associated with an aircraft powersystem or an electrical energy storage system. Independent sensors mayinclude separate sensors measuring physical or electrical quantitiesthat may be powered by and/or in communication with circuitsindependently, where each may signal sensor output to a control circuitsuch as a user graphical interface. In an embodiment, use of a pluralityof independent sensors may result in redundancy configured to employmore than one sensor that measures the same phenomenon, those sensorsbeing of the same type, a combination of, or another type of sensor notdisclosed, so that in the event one sensor fails, the ability to detectphenomenon is maintained and in a non-limiting example, a user alteraircraft usage pursuant to sensor readings. At least a sensor may beconfigured to detect pilot input from at least pilot control. At leastpilot control may include a throttle lever, inceptor stick, collectivepitch control, steering wheel, brake pedals, pedal controls, toggles,joystick. One of ordinary skill in the art, upon reading the entirety ofthis disclosure would appreciate the variety of pilot input controlsthat may be present in an electric aircraft consistent with the presentdisclosure. Inceptor stick may be consistent with disclosure of inceptorstick in U.S. Pat. App. Ser. No. 17/001,845 and titled “A HOVER ANDTHRUST CONTROL ASSEMBLY FOR DUAL-MODE AIRCRAFT”, which is incorporatedherein by reference in its entirety. Collective pitch control may beconsistent with disclosure of collective pitch control in U.S. Pat. App.Ser. No. 16/929,206 and titled “HOVER AND THRUST CONTROL ASSEMBLY FORDUAL-MODE AIRCRAFT”, which is incorporated herein by reference in itsentirety. At least pilot control may be physically located in thecockpit of the aircraft or remotely located outside of the aircraft inanother location communicatively connected to at least a portion of theaircraft. “Communicatively connect”, for the purposes of thisdisclosure, is a process whereby one device, component, or circuit isable to receive data from and/or transmit data to another device,component, or circuit; communicative connecting may be performed bywired or wireless electronic communication, either directly or by way ofone or more intervening devices or components. In an embodiment,communicative connecting includes electrically coupling an output of onedevice, component, or circuit to an input of another device, component,or circuit. Communicative connecting may be performed via a bus or otherfacility for intercommunication between elements of a computing device.Communicative connecting may include indirect connections via “wireless”connection, low power wide area network, radio communication, opticalcommunication, magnetic, capacitive, or optical coupling, or the like.At least pilot control may include buttons, switches, or other binaryinputs in addition to, or alternatively than digital controls aboutwhich a plurality of inputs may be received. At least pilot control maybe configured to receive pilot input. Pilot input may include a physicalmanipulation of a control like a pilot using a hand and arm to push orpull a lever, or a pilot using a finger to manipulate a switch. Pilotinput may include a voice command by a pilot to a microphone andcomputing system consistent with the entirety of this disclosure.

With continued reference to FIG. 1 , at least a sensor 108 is configuredto detect a torque datum 108. At least a sensor may include, torquesensor, gyroscope, accelerometer, torque sensor, magnetometer, inertialmeasurement unit (IMU), pressure sensor, force sensor, proximity sensor,displacement sensor, vibration sensor, among others. At least a sensor104 may include a sensor suite which may include a plurality of sensorsthat may detect similar or unique phenomena. For example, in anon-limiting embodiment, sensor suite may include a plurality ofaccelerometers, a mixture of accelerometers and gyroscopes, or a mixtureof an accelerometer, gyroscope, and torque sensor. For the purposes ofthe disclosure, a “torque datum” is an element of data representing oneor more parameters detailing power, energy, force, and/or torque outputby one or more propulsors, flight components, or other elements of anelectric aircraft. Torque datum 112 may indicate the torque output of atleast a propulsor 116. At least a propulsor 116 may include anypropulsor as described herein. In embodiment, at least a propulsor 116may include an electric motor, a propeller, a jet engine, a paddlewheel, a rotor, turbine, or any other mechanism configured to manipulatea fluid medium to propel an aircraft as described herein. Torque datum112 may indicate the torque of a rotor shaft attached to turbine rotorsof one or more propulsors, such as at least a propulsor 116.

With continued reference to FIG. 1 , torque datum 112 may be detected byutilizing a least squares method. A least squares method may utilize theequation

y = Af + d

Which represents the least-squares standard form. If the disturbance “d”has a Gaussian distribution, then typical least squares may be appliedto solve it, and the solutions is the maximum likelihood estimator. Forthe purposes of this disclosure, “maximum likelihood estimator” is amethod of estimating the parameters of a probability distribution bymaximizing a likelihood function, so that under the assumed statisticalmodel the observed data is most probable, in this cast torque datum 112,or the lack thereof. Gaussian distribution is a type of continuousprobability distribution for a real-valued random variable. The generalform of its probability density function is

$f(x) + \frac{1}{\sigma\sqrt{2\pi}}e^{- \frac{1}{2}{(\frac{({x - \mu})}{\sigma})}^{2}}$

wherein a random variable with a Gaussian distribution is said to benormally distributed and is called a normal deviate. Least squaresmethod to detect fault in an electric aircraft may include standardbatch least squares, which requires a running window of past datarepresented torque outputs of at least a propulsor 116 in a buffer.Additionally, or alternatively, recursive least squares with forgettingmay be employed, which requires data only at the current timestep inorder to detect torque outputs from at least a propulsor 116. Forexample, and without limitation, after running the least squaresanalysis, the elements of “f” that are near to zero value may indicate afaulty at least a propulsor 116. If such a fault is detected formultiple timesteps, a fault may be detected. For example, and withoutlimitation, some filtering may be required to reduce noise in the datacollected over series of least squares analysis. Least squares may beemployed in regression analysis to approximate a solution ofoverdetermined systems (sets of equations in which there are moreequations than unknowns) by minimizing a sum of the squares of theresiduals made in the results of every single equation. Least squaresmay be used in data fitting. A best fit in the least-squares sense mayminimize a sum of squared residuals (a residual being: the differencebetween an observed value, and the fitted value provided by a model).When a problem has substantial uncertainties in an independent variableand/or variable set (the x variable), then simple regression andleast-squares methods may be insufficient; in such cases, methodologyemployed for fitting errors-in-variables models may be consideredinstead of that for least squares. Least-squares problems may fall intotwo categories: linear or ordinary least squares and nonlinear leastsquares, depending on whether or not the residuals are linear in allunknowns. Linear least-squares problem may occur in statisticalregression analysis; it has a closed-form solution. Nonlinear problemmay be solved by iterative refinement; at each iteration a system may beapproximated by a linear one, and thus a core calculation may be similarin both cases. Polynomial least squares may describe a variance in aprediction of a dependent variable as a function of an independentvariable and deviations from a fitted curve. When observations come froman exponential family and mild conditions are satisfied, least-squaresestimates, and maximum-likelihood estimates may be identical. Leastsquares may also be derived as a method of moments estimator.

With continued reference to FIG. 1 , system 100 includes at least asensor 108 which may be configured to constantly search for a change intorque output characterized by torque datum 112 associated with at leasta propulsor 116 over a period of time. In other words, if a torque datumis lower than the previous detected torque datum, then a loss of torquemay be detected by at least a sensor 108. For the purposes of thisdisclosure, “loss of torque detection” is detection of torque outputthat is less, or less by some threshold proportion and/or amount, thanan expected torque output for at least a propulsor. One of ordinaryskill in the art, upon reviewing the entirety of this disclosure, willunderstand that detection of torque output that is less than an expectedtorque value may include a torque output of zero torque or loss ofcommunication with the at least a propulsor 116. Loss of torque mayindicate that at least a propulsor 116 associated with torque datum 112is experiencing a fault or has become inoperable. For the purposes ofthis disclosure, “one rotor inoperable (ORI)” is a failure state whereinat least a propulsor is not operating at sufficient power output tooperate the aircraft normally in its default control mode. At least asensor 108 may indicate that at least a propulsor is experiencing afault if multiple torque datum 112 is detected in a row outside of anormal range.

With continued reference to FIG. 1 , system 100 includes an observer120. Observer 120 may be an aircraft motion observer as described inreference to FIG. 2 . Observer 120 may be similar to or the same as theaircraft motion observer as described in U.S. Pat. App. Ser. No.17/218,403 filed on Mar. 31, 2021 and titled, “AIRCRAFT MOTION OBSERVERCONFIGURED FOR USE IN ELECTRIC AIRCRAFT” which is incorporated byreference herein in its entirety. Observer 120 may include one or morecircuit elements, computing devices, FGPAs, or other electronic devicesconfigured to generate torque prediction datum 124. Any module asdescribed herein, including without limitation observer and/or mixer,may be created using any combination of hardware and/or software logiccommands, and may be physically or conceptually separate from or mergedwith any other such module, as persons skilled in the art willappreciate upon reviewing the entirety of this disclosure. For thepurposes of this disclosure, a “torque prediction datum” is one or moreelements of data generated by the system that represents an expectedtorque output or range of torque outputs associated with at least apropulsor. Torque prediction datum 124 may constantly be output byobserver 120 adjusting for aircraft maneuvers and requirementsthroughout an aircraft’s flight envelope such as take-off, banking,climbing, transition from hover to forward flight, transition fromforward flight to hover, landing, or other aircraft maneuvers. Observer120 is configured to compare torque datum 112 and torque predictiondatum 124 for at least a propulsor. Observer 120 may be configured tocompare torque datum 112 and torque prediction datum 124 utilizingsubtraction. In non-limiting embodiments, subtraction may includesubtracting torque datum 112 from torque prediction datum 124. Innon-limiting embodiments, subtraction may include subtracting torqueprediction datum 124 from torque datum 112. Observer 120 may beconfigured to compare torque datum 112 and torque prediction datum 124utilizing ratios. In non-limiting embodiments, ratios may include theratio of torque datum 112 to torque prediction datum 124. Innon-limiting embodiments, ratios may include the ration of torqueprediction datum 124 to torque datum 112. Observer 120 may be configuredto compare torque datum 112 and torque prediction datum 124 utilizingaddition. In non-limiting embodiments, addition may include addingtorque datum 112 and torque prediction datum 124 and comparing the totalto a predetermined threshold datum. The comparison may take place at onepoint in a flight envelope, constantly with adjusted detected readingsand predictions, at regular intervals, when commanded to do so by apilot, user, or computer, or a combination thereof. Observer 120 may beconfigured to compare torque datum 112 and torque prediction datum 124at regular intervals such as every second, every minute, every fiveminutes, or at a predetermined time interval. Observer 120 is configuredto generate residual datum 128. For the purposes of this disclosure,“residual datum” is one or more elements of data representing adifference in a predicted torque and detected torque output of at leasta propulsor. In non-limiting embodiments, residual datum 128 may includea difference between torque datum 112 and torque prediction datum 124 asrepresented by any of the mathematical operations as described herein.Residual datum 128 may indicate that a detected torque datum 112 is lessthan torque prediction datum 124 which may indicate that at least apropulsor is experiencing a fault and the aircraft may be in ORI.Residual datum 128 may indicate that a detected torque datum 112 is morethan torque prediction datum 124 indicated that at least a propulsor isexperiencing a fault, at least a sensor 108 is experiencing a fault, oranother aircraft state. Residual datum 128 may indicate that torqueprediction datum 124, torque datum 112, or another element of data isout of range, greater than a predetermined threshold, lower than apredetermined threshold, or the like.

With continued reference to FIG. 1 , system 100 includes mixer 132.Mixer 132 may include one or more circuit elements, one or morecomputing devices or portions thereof, or other electronic componentsconfigured to receive one set of data and allocate a second set of dataas an output. In embodiments, mixer 132 may receive a pilot input asdescribed above indicated a desired change in aircraft trajectory andoutput the torque output by at least a propulsor 116 to accomplish saidtrajectory change. With continued reference to FIG. 1 , a “mixer”, forthe purposes of this disclosure, is a component that takes in at leastan incoming signal, such as initial vehicle torque signal includingplurality of attitude command and allocates one or more outgoingsignals, such as modified attitude commands and output a torque command,or the like, to at least a propulsor, flight component, or one or morecomputing devices connected thereto. One of ordinary skill in the art,after reading the entirety of this disclosure, would be aware that amixer, as used herein, may additionally or alternatively be described asperforming “control allocation” or “torque allocation”. For example,mixer 132 may take in commands to alter aircraft trajectory thatrequires a change in pitch and yaw. Mixer 132 may allocate torque to atleast one propulsor (or more) that do not independently alter pitch andyaw in combination to accomplish the command to change pitch and yaw.More than one propulsor may be required to adjust torques to accomplishthe command to change pitch and yaw, mixer 132 would take in the commandand allocate those torques to the appropriate propulsors consistent withthe entirety of this disclosure. One of ordinary skill in the art, afterreading the entirety of this disclosure, will appreciate the limitlesscombination of propulsors, flight components, control surfaces, orcombinations thereof that could be used in tandem to generate someamount of authority in pitch, roll, yaw, and lift of an electricaircraft consistent with this disclosure. Mixer 132 may be a nonlinearprogram-based mixer that create new frequencies from two signals appliedto it. In most applications, two signals are applied to mixer 132, andit produces new signals at the sum and difference of the originalfrequencies. Other frequency component may also be produced in apractical frequency mixer. One of ordinary skill in the art wouldunderstand that, in general, mixers are widely used to shift signalsfrom one frequency range to another, a process known as heterodyning.Another form of mixer operates by switching, with the smaller inputsignal being passed inverted or noninverted according to the phase ofthe local oscillator (LO). This would be typical of the normal operatingmode of a packaged double balanced mixer, with the local oscillatordrive considerably higher than the signal amplitude. Mixer 132 may beconsistent with any mixer described herein. Mixer 132 may be implementedusing an electrical logic circuit. “Logic circuits”, for the purposes ofthis disclosure, refer to an arrangement of electronic components suchas diodes or transistors acting as electronic switches configured to acton one or more binary inputs that produce a single binary output. Logiccircuits may include devices such as multiplexers, registers, arithmeticlogic units (ALUs), computer memory, and microprocessors, among others.In modern practice, metal-oxide-semiconductor field-effect transistors(MOSFETs) may be implemented as logic circuit components. Mixer 132 maybe implemented using a processor. Mixer 132 is configured to receive aninitial vehicle torque signal for at least a propulsor from flightcontroller 104. Mixer 132 may solve at least an optimization problem. Atleast an optimization problem may include solving the pitch momentfunction that may be a nonlinear program.

With continued reference to FIG. 1 , mixer 132 may include an inertiacompensator. An inertia compensator may include one or more computingdevices, an electrical component, circuitry, one or more logic circuitsor processors, or the like, which may configured to compensate forinertia in one or more propulsors present in system 100. For thepurposes of this disclosure, an “inertia compensator” is one or morecomputing devices, electrical circuits, processors, or the likeconfigured to compensate torque output signals for massive flightcomponents. In non-limiting examples, inertia compensator may increase atorque output signal to get a rotor to spinning and then quickly lowerthe torque output signal once the rotor has gained momentum and itsinertia tends to keep the rotor spinning, consistent with the entiretyof this disclosure. Mixer 132 is configured, in general, to outputsignals and command propulsors to produce a certain amount of torque;however, real-world propulsors contain mass, and therefore have inertia.“Inertia”, for the purposes of this disclosure, is a property of matterby which it continues in its existing state of rest or uniform motion ina straight line, unless that state is changed by an external force.Specifically, in this case, a massive object requires more force ortorque to start motion than is required to continue producing torque. Ina control system, mixer 132 must therefore modulate the would-be signalto account for inertia of the physical system being commanded. Theinertia compensator may make appropriate calculations based on modifiedattitude command 132, output torque commands, and other considerationslike environmental conditions, available power, vehicle torque limits,among others. Inertia compensator may adjust vehicle torque limits forcertain periods of time wherein, for example, output torque commands maybe allowed to overspeed a propulsor to start the propulsor’s rotatingphysical components and then quickly step down the torque as required tomaintain the commanded torque. The inertia compensator which may includea lead filter.

With continued reference to FIG. 1 , mixer 132 is configured togenerate, as a function of residual datum 128, a torque priority commanddatum 136. For the purposes of this disclosure, “torque priority commanddatum” is one or more elements of data generated in order to compensatefor a change in torque in at least a propulsor. For example, and withoutlimitation, torque priority command datum 136 may be generated inresponse to residual datum 128 that indicates at least a propulsor 116has lost all torque capabilities. In this example, torque prioritycommand datum 136 would command another propulsor or flight component toincrease in order to maintain an aircraft’s safety until emergencyprocedures such as landing can be accomplished. In non-limitingexamples, torque priority command datum 136 may include commanding anopposite propulsor or flight component to increase in response to arotor losing some torque capability, such as loss of torque detectionconsistent with the entirety of this disclosure. In further non-limitingexamples, torque priority command datum 136 may include increasingtorque to all propulsors functioning in a given fault situation in orderto compensate for one propulsor 116 outputting a lower torque outputthan required. For example and without limitation, torque prioritycommand datum 136 may change more than one propulsor’s torque output inorder to compensate for a loss in torque in at least a propulsor 116. Inembodiments, when aircraft includes a quadcopter configuration, if thefront right propulsor loses torque, and therefore lift is reduced, thefront left and back right propulsors may increase their torque tomaintain lift and the back left propulsor may overspeed in reverse toprovide negative lift to prevent the aircraft from tipping in theno-lift corner. One of ordinary skill in the art would appreciate thatthis is only one arrangement of propulsors and does not limit thearrangement, number, or methodology of utilizing predicted and detecteddata to modify torque output to control an aircraft under ORIconditions.

With continued reference to FIG. 1 , mixer 132 may include a first modeconfigured to control a first plurality of propulsors consistent with atleast a propulsor 116. The first mode may be configured to control fourpropulsors in embodiments. One of ordinary skill in the art, afterreading the entirety of this disclosure, would be aware that a mixer inthe first mode may perform “control allocation” or “torque allocation”with the first plurality of propulsors, in the above example, four. Forexample, mixer 132 may take in commands to alter aircraft trajectorythat requires a change in pitch and yaw utilizing the first plurality ofpropulsors. Mixer 132 may allocate torque to the first plurality ofpropulsors that do not independently alter pitch and yaw in combinationto accomplish the command to change pitch and yaw. First plurality ofpropulsors may be required to adjust torques to accomplish the commandto change pitch and yaw, mixer 132 would take in the command andallocate those torques to the appropriate propulsors consistent with theentirety of this disclosure. One of ordinary skill in the art, afterreading the entirety of this disclosure, will appreciate the limitlesscombination of propulsors, flight components, control surfaces, orcombinations thereof that could be used in tandem to generate someamount of authority in pitch, roll, yaw, and lift of an electricaircraft consistent with this disclosure.

In embodiments, under ORI conditions, when at least a propulsor 116 ofthe four propulsors does not have adequate torque output capabilities,mixer 132 may switch to a second mode configured to control a secondplurality of propulsors, which may include, in an illustrative example,three remaining propulsors. Mixer 132 may continue receiving residualdatums 128 under this three propulsor configuration and allocate torqueaccordingly without consideration of the at least a propulsor 116experiencing a fault. One of ordinary skill in the art, after readingthe entirety of this disclosure, would be aware that a mixer in thesecond mode may perform “control allocation” or “torque allocation” withthe second plurality of propulsors, in this example, three. For example,mixer 132 may take in commands to alter aircraft trajectory thatrequires a change in pitch and yaw utilizing the second plurality ofpropulsors, as in this example, one propulsor has experienced a faultand is now inoperable. Mixer 132 may allocate torque to the secondplurality of propulsors that do not independently alter pitch and yaw incombination to accomplish the command to change pitch and yaw. Firstplurality of propulsors may be required to adjust torques to accomplishthe command to change pitch and yaw, mixer 132 would take in the commandand allocate those torques to the appropriate propulsors consistent withthe entirety of this disclosure. One of ordinary skill in the art, afterreading the entirety of this disclosure, will appreciate the limitlesscombination of propulsors, flight components, control surfaces,inoperable propulsors, or combinations thereof that could be used intandem to generate some amount of authority in pitch, roll, yaw, andlift of an electric aircraft consistent with this disclosure. Mixer 132is configured torque priority command datum 136 to at least a propulsor116 as a function of the torque allocation considering the torque datum112 detected.

Referring now to FIG. 2 , an embodiment for an observer 200 for anaircraft observer configured for use in electric aircraft is presentedin block diagram form. Observer 200 includes at least as aircraftcommand 204. For the purposes of this disclosure, an “aircraft command”is a desire of a pilot, user, or operator of a vehicle to change thevehicles trajectory, path, power output, or the like. Observer 200includes feedforward term 208 which may be the same or similar to anyfeedforward term as described herein. A “feedforward term”, for thepurposes of this disclosure, is any and all terms within a controldiagram that proceeds forward in a control loop instead of backwards.With feedforward control, the disturbances are measured and accountedfor before they have time to affect the system. In an example, such as ahouse thermostat as described above, a feed-forward system may measurethe fact that the door is opened and automatically turn on the heaterbefore the house can get too cold. Feed-forward control may be effectivewhere effects of the disturbances on the system must be accuratelypredicted. For instance, if a window was opened that was not beingmeasured, a feed-forward-controlled thermostat might let the house cooldown. A feedforward control system may operate faster than a feedbackcontrol system, which differs from the former in that it includes bothfeedforward signals and feedback signals. However, feedback controlsystems may generally be more controllable and more accurate because ofan ability to compare control outputs to sensed inputs using feedbacksignals, which permits modification of the latter to minimize errorbased on comparison. An observer is a feedback system that modifies amodel used in feedforward control to account for sources of error that afeedback loop would otherwise detect. This may produce a system that hasthe speed advantages of feedforward control without sacrificing thecontrollability and/or of feedback control; the motion observer itselfmay be taught using a feedback loop, for instance and without limitationas described in this disclosure.

With continued reference to FIG. 2 , observer 200 includes plant model212. Plant model 212 may include an actuator model similar to or thesame as actuator model. Plant model 212 may include one or more actuatormodels consistent with any actuator model as described herein. A “plantmodel”, for the purposes of this disclosure, is a component of controltheory which includes a process and an actuator. A plant is oftenreferred to with a transfer function which indicates the relationbetween an input signal and the output signal of a system withoutfeedback, commonly determined by physical properties of the system. In asystem with feedback, as in illustrative embodiments, herein described,the plant still has the same transfer function, but a control unit and afeedback loop, which possess their own transfer functions, are added tothe system. Plant model 212 may use rigid body mechanics and kinematicsas previously described, or another undisclosed method of modelingthree-dimensional bodies subject to flows, such as computational flowdynamics analysis, which may include flight component CFD as describedpreviously in regard to actuator model. Plant model 212 is configured togenerate predictive datum 216 consistent with any predictive datum asdescribed herein such that the predictive datum represents predictedbehavior of the aircraft subject to certain flows given at least anaircraft command 204. Observer state 224 may be consistent with observerstate 224 wherein it may represent predicted behavior of aircraftmotion. Observer 200 includes at least a sensor 228 configured to detectmeasured state datum 232 which may be consistent with the one or moresensors described in regard to sensor 228 and measured state datum 232describing the real-world behavior of the aircraft in response to atleast an aircraft command 204. Observer 200 includes controller 236which may be the same or similar to controller 236 configured togenerate inconsistency datum 240 which may be the same as or similar toinconsistency datum 240 which represents a compensation between how wellpredictive datum predicted the measured state datum. That is to say thatthe inconsistency datum compensates for the subsequent prediction fromthe plant model based on how accurately the previous plant model’sprediction represented the measured state datum of the real-worldaircraft.

With continued reference to FIG. 2 , a “performance datum”, for thepurposes of this disclosure, is a mathematical datum or set of data thatpresents the resultant forces, torques, or other interactions betweenthe plurality of flight components and the fluid flow in order topredict the behavior of the flight components during performance.Performance datum may be represented by one or more numbers, values,matrices, vectors, mathematical expressions, or the like for use in oneor more components of observer 200. Performance datum may be anelectrical signal capable of use by one or more components of observer200. Performance datum may be an analog or digital signal. Observer 200may include electronics, electrical components, or circuits configuredto condition signals for use between one or more components presentwithin system like analog to digital converters (ADC), digital to analogconverters (DAC), and the like.

With continued reference to FIG. 2 , at least an aircraft command 204indicates a desired change in aircraft heading or thrust, flightcontroller translates pilot input. That is to say that flight controllermay be configured to translate a pilot input, in the form of moving aninceptor stick, for example, into electrical signals to at least anactuator that in turn, moves at least a portion of the aircraft in a waythat manipulates a fluid medium, like air, to accomplish the pilot’sdesired maneuver. At least a portion of the aircraft that an actuatormoves may be a control surface. An actuator, or any portion of anelectric aircraft may include one or more flight controllers configuredto perform any of the operations described herein and communicate witheach of the other flight controllers, controllers, and other portions ofan electric aircraft.

With continued reference to FIG. 2 , a “control surface” as describedherein, is any form of amechanical/hydraulic/pneumatic/electronic/electromechanical linkage witha surface area that interacts with forces to move an aircraft. A controlsurface may include, as a non-limiting example, ailerons, flaps, leadingedge flaps, rudders, elevators, spoilers, slats, blades, stabilizers,stabilators, airfoils, a combination thereof, or any other mechanicalsurface are used to control an aircraft in a fluid medium. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various mechanical linkages that may be used as a controlsurface, as used and described in this disclosure.

With continued reference to FIG. 2 , in embodiments, feedforwardcontrol, the disturbances are measured and accounted for before theyhave time to affect the system. In an example, such as a housethermostat as described above, a feed-forward system may measure thefact that the door is opened and automatically turn on the heater beforethe house can get too cold. Feed-forward control may be effective whereeffects of the disturbances on the system must be accurately predicted.For instance, if a window was opened that was not being measured, afeed-forward-controlled thermostat might let the house cool down. Afeedforward control system may operate faster than a feedback controlsystem, which differs from the former in that it includes bothfeedforward signals and feedback signals. However, feedback controlsystems may generally be more controllable and more accurate because ofan ability to compare control outputs to sensed inputs using feedbacksignals, which permits modification of the latter to minimize errorbased on comparison. An observer is a feedback system that modifies amodel used in feedforward control to account for sources of error that afeedback loop would otherwise detect. This may produce a system that hasthe speed advantages of feedforward control without sacrificing thecontrollability and/or of feedback control; the motion observer itselfmay be taught using a feedback loop, for instance and without limitationas described in this disclosure.

With continued reference to FIG. 2 , plant model 212 may include aNewton Euler computational flow dynamic model (CFD). A Newton Euler CFDmay include a model in which a plurality of flows may be simulated overa plurality of flight components over the entire range of motion of theflight components and the resultant torques and forces generatedtherefrom may be modeled. CFD analysis may be the same or similar to CFDanalysis described in this disclosure with regard to actuator model.Flight components used in a Newton Euler CFD may be any of the flightcomponents as described in this disclosure, including but not limitedto, actuators, control surfaces, geometries related to an aircraft, andthe like, among others. The “flows” for the purposes of this disclosure,is the flow of a liquid or gas over a physical body with a volume. Flowsmay include any fluid with the necessary viscosity to flow over a solidbody. Flow may include inviscid flow, turbulent flow, incompressibleflow, compressible flow, and laminar flow, among others. CFD analysismay also include and/or model resultant torques and forces on anaircraft in one or more orientations with respect to flow. “Laminarflow”, for the purposes of this disclosure, is characterized by fluidparticles following smooth paths in layers, with each layer movingsmoothly past the adjacent layers with little or no mixing. “Turbulentflow”, for the purposes of this disclosure, is fluid motioncharacterized by chaotic changes in pressure and flow velocity; this mayrepresent a contrast to a laminar flow, which occurs when a fluid flowsin parallel layers, with no disruption between those layers. “Inviscidflow”, for the purposes of this disclosure, is the flow of an inviscidfluid, in which the viscosity of the fluid is equal to zero.“Incompressible flow”, for the purposes of this disclosure, is a flow inwhich the material density is constant within a fluid parcel aninfinitesimal volume that moves with the flow velocity. An equivalentstatement that implies incompressibility is that the divergence of theflow velocity is zero. “Compressible flow”, for the purposes of thisdisclosure, is a flow having a significant change in fluid density.While all flows are compressible in real life, flows may be treated asbeing incompressible when the Mach number is below 0.3.

With continued reference to FIG. 2 , a “predictive datum”, for thepurposes of this disclosure, is one or more elements of datarepresenting the reaction of the rigid body representing an electricaircraft based on the actuator model and performance datum. Predictivedatum 216 may be one or more vectors, coordinates, torques, forces,moments, or the like that represent the predicted movement or positionof the rigid body subject to the model fluid dynamics as a function ofthe performance datum. Predictive datum 216 may include, be correlatedwith, or be the data presenting movement, velocities, or torques on therigid body after application of fluid flows. Predictive datum 216 may begenerated as a function of angle of attack (AoA). “Angle of attack”, forthe purposes of this disclosure, is the relative angle between areference line on a body (herein the rigid body), and the vectorrepresenting the relative motion between the body and the fluid throughwhich it is moving. In other words, angle of attack, is the anglebetween the body’s reference line and the oncoming flow. The referenceline may include the farthest two points on the rigid body such that theline approximates the length of the rigid body. In the context orairfoils, the reference line may be the chord line, which connects theleading edge and the trailing edge of the airfoil. Plant model 212 maybe configured to generate predictive datum 216 as a function of a signalfrom at least a flight component. A signal may include a position of oneor more flight components such as control surfaces, throttle position,propulsor output, any datum associated with the aircraft, and any pilotcommand datum as described herein, among others. In situations whereangle of attack is not useful, not available, or in general when it isnot advantageous to use angle of attack as an input to the plant model212, throttle position and/or a signal from one of the plurality offlight components may be used as a proxy. There may be data thatcorrelates throttle position to angle of attack and/or airspeed that maybe used as a suitable input to plant model 212. Airspeed may also beused as a suitable proxy for flow types in certain situations whereother parameters are unavailable. Airspeed may be used separately or incombination with other inputs. An “airspeed”, for the purposes of thisdisclosure, is the speed of a body moving through the fluid relative tothe fluid. The throttle may be consistent with any throttle or otherpilot control as discussed herein. This in no way precludes the use ofother proxies for plant model 212 inputs such as collective pitch orother pilot inputs alone or in combination.

With continued reference to FIG. 2 , a “measured state datum”, for thepurposes of this disclosure, is one or more elements of datarepresenting the actual motion/forces/moments/torques acting on theaircraft in the real world as a function of the at least an aircraftcommand 204. A measured state datum 232 includes an inertial measurementunit. An “inertial measurement unit”, for the purposes of thisdisclosure, is an electronic device that measures and reports a body’sspecific force, angular rate, and orientation of the body, using acombination of accelerometers, gyroscopes, and magnetometers, in variousarrangements and combinations. Sensor 228 measures the aircraft’s actualresponse in the real world to the at least an aircraft command 204.Sensor 228 may include a motion sensor. A “motion sensor”, for thepurposes of this disclosure, is a device or component configured todetect physical movement of an object or grouping of objects. One ofordinary skill in the art would appreciate, after reviewing the entiretyof this disclosure, that motion may include a plurality of typesincluding but not limited to: spinning, rotating, oscillating, gyrating,jumping, sliding, reciprocating, or the like. Sensor 228 may include,torque sensor, gyroscope, accelerometer, torque sensor, magnetometer,inertial measurement unit (IMU), pressure sensor, force sensor,proximity sensor, displacement sensor, vibration sensor, among others.Sensor 228 may include a sensor suite which may include a plurality ofsensors that may detect similar or unique phenomena. For example, in anon-limiting embodiment, sensor suite may include a plurality ofaccelerometers, a mixture of accelerometers and gyroscopes, or a mixtureof an accelerometer, gyroscope, and torque sensor. The herein disclosedsystem and method may comprise a plurality of sensors in the form ofindividual sensors or a sensor suite working in tandem or individually.A sensor suite may include a plurality of independent sensors, asdescribed herein, where any number of the described sensors may be usedto detect any number of physical or electrical quantities associatedwith an aircraft power system or an electrical energy storage system.Independent sensors may include separate sensors measuring physical orelectrical quantities that may be powered by and/or in communicationwith circuits independently, where each may signal sensor output to acontrol circuit such as a user graphical interface. In an embodiment,use of a plurality of independent sensors may result in redundancyconfigured to employ more than one sensor that measures the samephenomenon, those sensors being of the same type, a combination of, oranother type of sensor not disclosed, so that in the event one sensorfails, the ability to detect phenomenon is maintained and in anon-limiting example, a user alter aircraft usage pursuant to sensorreadings.

Controller 236 is configured to compare the predictive datum 216, i.e.,one or more elements of observer state 224, and the measured state datum232. Controller 236 may include one or more circuit elementscommunicatively and electrically connected to one or more componentsdescribed herein. Controller 236 may perform one or more mathematicaloperations, manipulations, arithmetic, machine-learning, or acombination thereof on one or more elements of data. Controller 236generates, as a function of the comparing, generate inconsistency datum240 wherein inconsistency datum 240 includes a mathematical function tocompensate for the difference between the predictive datum 216 and themeasured state datum 232. Controller 236 is configured to compensate forthe difference between predictive datum 216, which is the prediction ofthe behavior of the aircraft and the actual behavior of the aircraft ascharacterized by measured state datum 232. Controller 236 generatesinconsistency datum 240 such that the inconsistency datum 240 on thesubsequent control loop can be an input to plant model 212 andpreemptively adjust predicted datum 216 as to more accurately predictaircraft behavior. In a non-limiting illustrative example, if plantmodel 212 generates the perfect predictive datum 216, such that itperfectly predicts the aircraft behavior given the at least an aircraftcommand 204, performance datum, then the measured state datum 232detected by sensor 228 would represent the same quantities. Thereforecontroller 236 would generate inconsistency datum 240 that would notprovide any additional compensation on the subsequent control loop.

Now referring to FIG. 3 , an exemplary embodiment 300 of a flightcontroller 304 is illustrated. As used in this disclosure a “flightcontroller” is a computing device of a plurality of computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and flight instruction. Flight controller 304 may includeand/or communicate with any computing device as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Further, flight controller 304may include a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. In embodiments, flight controller 304 may be installed in anaircraft, may control the aircraft remotely, and/or may include anelement installed in the aircraft and a remote element in communicationtherewith.

In an embodiment, and still referring to FIG. 3 , flight controller 304may include a signal transformation component 308. As used in thisdisclosure a “signal transformation component” is a component thattransforms and/or converts a first signal to a second signal, wherein asignal may include one or more digital and/or analog signals. Forexample, and without limitation, signal transformation component 308 maybe configured to perform one or more operations such as preprocessing,lexical analysis, parsing, semantic analysis, and the like thereof. Inan embodiment, and without limitation, signal transformation component308 may include one or more analog-to-digital convertors that transforma first signal of an analog signal to a second signal of a digitalsignal. For example, and without limitation, an analog-to-digitalconverter may convert an analog input signal to a 10-bit binary digitalrepresentation of that signal. In another embodiment, signaltransformation component 308 may include transforming one or morelow-level languages such as, but not limited to, machine languagesand/or assembly languages. For example, and without limitation, signaltransformation component 308 may include transforming a binary languagesignal to an assembly language signal. In an embodiment, and withoutlimitation, signal transformation component 308 may include transformingone or more high-level languages and/or formal languages such as but notlimited to alphabets, strings, and/or languages. For example, andwithout limitation, high-level languages may include one or more systemlanguages, scripting languages, domain-specific languages, visuallanguages, esoteric languages, and the like thereof. As a furthernon-limiting example, high-level languages may include one or morealgebraic formula languages, business data languages, string and listlanguages, object-oriented languages, and the like thereof.

Still referring to FIG. 3 , signal transformation component 308 may beconfigured to optimize an intermediate representation 312. As used inthis disclosure an “intermediate representation” is a data structureand/or code that represents the input signal. Signal transformationcomponent 308 may optimize intermediate representation as a function ofa data-flow analysis, dependence analysis, alias analysis, pointeranalysis, escape analysis, and the like thereof. In an embodiment, andwithout limitation, signal transformation component 308 may optimizeintermediate representation 312 as a function of one or more inlineexpansions, dead code eliminations, constant propagation, looptransformations, and/or automatic parallelization functions. In anotherembodiment, signal transformation component 308 may optimizeintermediate representation as a function of a machine dependentoptimization such as a peephole optimization, wherein a peepholeoptimization may rewrite short sequences of code into more efficientsequences of code. Signal transformation component 308 may optimizeintermediate representation to generate an output language, wherein an“output language,” as used herein, is the native machine language offlight controller 304. For example, and without limitation, nativemachine language may include one or more binary and/or numericallanguages.

In an embodiment, and without limitation, signal transformationcomponent 308 may include transform one or more inputs and outputs as afunction of an error correction code. An error correction code, alsoknown as error correcting code (ECC), is an encoding of a message or lotof data using redundant information, permitting recovery of corrupteddata. An ECC may include a block code, in which information is encodedon fixed-size packets and/or blocks of data elements such as symbols ofpredetermined size, bits, or the like. Reed-Solomon coding, in whichmessage symbols within a symbol set having q symbols are encoded ascoefficients of a polynomial of degree less than or equal to a naturalnumber k, over a finite field F with q elements; strings so encoded havea minimum hamming distance of k+1, and permit correction of (q-k-1)/2erroneous symbols. Block code may alternatively or additionally beimplemented using Golay coding, also known as binary Golay coding,Bose-Chaudhuri, Hocquenghuem (BCH) coding, multidimensional parity-checkcoding, and/or Hamming codes. An ECC may alternatively or additionallybe based on a convolutional code.

In an embodiment, and still referring to FIG. 3 , flight controller 304may include a reconfigurable hardware platform 316. A “reconfigurablehardware platform,” as used herein, is a component and/or unit ofhardware that may be reprogrammed, such that, for instance, a data pathbetween elements such as logic gates or other digital circuit elementsmay be modified to change an algorithm, state, logical sequence, or thelike of the component and/or unit. This may be accomplished with suchflexible high-speed computing fabrics as field-programmable gate arrays(FPGAs), which may include a grid of interconnected logic gates,connections between which may be severed and/or restored to program inmodified logic. Reconfigurable hardware platform 316 may be reconfiguredto enact any algorithm and/or algorithm selection process received fromanother computing device and/or created using machine-learningprocesses.

Still referring to FIG. 3 , reconfigurable hardware platform 316 mayinclude a logic component 320. As used in this disclosure a “logiccomponent” is a component that executes instructions on output language.For example, and without limitation, logic component may perform basicarithmetic, logic, controlling, input/output operations, and the likethereof. Logic component 320 may include any suitable processor, such aswithout limitation a component incorporating logical circuitry forperforming arithmetic and logical operations, such as an arithmetic andlogic unit (ALU), which may be regulated with a state machine anddirected by operational inputs from memory and/or sensors; logiccomponent 320 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Logic component 320 may include,incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating-point unit (FPU), and/or system on achip (SoC). In an embodiment, logic component 320 may include one ormore integrated circuit microprocessors, which may contain one or morecentral processing units, central processors, and/or main processors, ona single metal-oxide-semiconductor chip. Logic component 320 may beconfigured to execute a sequence of stored instructions to be performedon the output language and/or intermediate representation 312. Logiccomponent 320 may be configured to fetch and/or retrieve the instructionfrom a memory cache, wherein a “memory cache,” as used in thisdisclosure, is a stored instruction set on flight controller 304. Logiccomponent 320 may be configured to decode the instruction retrieved fromthe memory cache to opcodes and/or operands. Logic component 320 may beconfigured to execute the instruction on intermediate representation 312and/or output language. For example, and without limitation, logiccomponent 320 may be configured to execute an addition operation onintermediate representation 312 and/or output language.

In an embodiment, and without limitation, logic component 320 may beconfigured to calculate a flight element 324. As used in this disclosurea “flight element” is an element of datum denoting a relative status ofaircraft. For example, and without limitation, flight element 324 maydenote one or more torques, thrusts, airspeed velocities, forces,altitudes, groundspeed velocities, directions during flight, directionsfacing, forces, orientations, and the like thereof. For example, andwithout limitation, flight element 324 may denote that aircraft iscruising at an altitude and/or with a sufficient magnitude of forwardthrust. As a further non-limiting example, flight status may denote thatis building thrust and/or groundspeed velocity in preparation for atakeoff. As a further non-limiting example, flight element 324 maydenote that aircraft is following a flight path accurately and/orsufficiently.

Still referring to FIG. 3 , flight controller 304 may include a chipsetcomponent 328. As used in this disclosure a “chipset component” is acomponent that manages data flow. In an embodiment, and withoutlimitation, chipset component 328 may include a northbridge data flowpath, wherein the northbridge dataflow path may manage data flow fromlogic component 320 to a high-speed device and/or component, such as aRAM, graphics controller, and the like thereof. In another embodiment,and without limitation, chipset component 328 may include a southbridgedata flow path, wherein the southbridge dataflow path may manage dataflow from logic component 320 to lower-speed peripheral buses, such as aperipheral component interconnect (PCI), industry standard architecture(ICA), and the like thereof. In an embodiment, and without limitation,southbridge data flow path may include managing data flow betweenperipheral connections such as ethernet, USB, audio devices, and thelike thereof. Additionally, or alternatively, chipset component 328 maymanage data flow between logic component 320, memory cache, and a flightcomponent 332. As used in this disclosure a “flight component” is aportion of an aircraft that can be moved or adjusted to affect one ormore flight elements. For example, flight component 332 may include acomponent used to affect the aircrafts’ roll and pitch which maycomprise one or more ailerons. As a further example, flight component332 may include a rudder to control yaw of an aircraft. In anembodiment, chipset component 328 may be configured to communicate witha plurality of flight components as a function of flight element 324.For example, and without limitation, chipset component 328 may transmitto an aircraft rotor to reduce torque of a first lift propulsor andincrease the forward thrust produced by a pusher component to perform aflight maneuver.

In an embodiment, and still referring to FIG. 3 , flight controller 304may be configured generate an autonomous function. As used in thisdisclosure an “autonomous function” is a mode and/or function of flightcontroller 304 that controls aircraft automatically. For example, andwithout limitation, autonomous function may perform one or more aircraftmaneuvers, take offs, landings, altitude adjustments, flight levelingadjustments, turns, climbs, and/or descents. As a further non-limitingexample, autonomous function may adjust one or more airspeed velocities,thrusts, torques, and/or groundspeed velocities. As a furthernon-limiting example, autonomous function may perform one or more flightpath corrections and/or flight path modifications as a function offlight element 324. In an embodiment, autonomous function may includeone or more modes of autonomy such as, but not limited to, autonomousmode, semi-autonomous mode, and/or non-autonomous mode. As used in thisdisclosure “autonomous mode” is a mode that automatically adjusts and/orcontrols aircraft and/or the maneuvers of aircraft in its entirety. Forexample, autonomous mode may denote that flight controller 304 willadjust the aircraft. As used in this disclosure a “semi-autonomous mode”is a mode that automatically adjusts and/or controls a portion and/orsection of aircraft. For example, and without limitation,semi-autonomous mode may denote that a pilot will control thepropulsors, wherein flight controller 304 will control the aileronsand/or rudders. As used in this disclosure “non-autonomous mode” is amode that denotes a pilot will control aircraft and/or maneuvers ofaircraft in its entirety.

In an embodiment, and still referring to FIG. 3 , flight controller 304may generate autonomous function as a function of an autonomousmachine-learning model. As used in this disclosure an “autonomousmachine-learning model” is a machine-learning model to produce anautonomous function output given flight element 324 and a pilot signal336 as inputs; this is in contrast to a non-machine learning softwareprogram where the commands to be executed are determined in advance by auser and written in a programming language. As used in this disclosure a“pilot signal” is an element of datum representing one or more functionsa pilot is controlling and/or adjusting. For example, pilot signal 336may denote that a pilot is controlling and/or maneuvering ailerons,wherein the pilot is not in control of the rudders and/or propulsors. Inan embodiment, pilot signal 336 may include an implicit signal and/or anexplicit signal. For example, and without limitation, pilot signal 336may include an explicit signal, wherein the pilot explicitly statesthere is a lack of control and/or desire for autonomous function. As afurther non-limiting example, pilot signal 336 may include an explicitsignal directing flight controller 304 to control and/or maintain aportion of aircraft, a portion of the flight plan, the entire aircraft,and/or the entire flight plan. As a further non-limiting example, pilotsignal 336 may include an implicit signal, wherein flight controller 304detects a lack of control such as by a malfunction, torque alteration,flight path deviation, and the like thereof. In an embodiment, andwithout limitation, pilot signal 336 may include one or more explicitsignals to reduce torque, and/or one or more implicit signals thattorque may be reduced due to reduction of airspeed velocity. In anembodiment, and without limitation, pilot signal 336 may include one ormore local and/or global signals. For example, and without limitation,pilot signal 336 may include a local signal that is transmitted by apilot and/or crew member. As a further non-limiting example, pilotsignal 336 may include a global signal that is transmitted by airtraffic control and/or one or more remote users that are incommunication with the pilot of aircraft. In an embodiment, pilot signal336 may be received as a function of a tri-state bus and/or multiplexorthat denotes an explicit pilot signal should be transmitted prior to anyimplicit or global pilot signal.

Still referring to FIG. 3 , autonomous machine-learning model mayinclude one or more autonomous machine-learning processes such assupervised, unsupervised, or reinforcement machine-learning processesthat flight controller 304 and/or a remote device may or may not use inthe generation of autonomous function. As used in this disclosure“remote device” is an external device to flight controller 304.Additionally, or alternatively, autonomous machine-learning model mayinclude one or more autonomous machine-learning processes that afield-programmable gate array (FPGA) may or may not use in thegeneration of autonomous function. Autonomous machine-learning processmay include, without limitation machine learning processes such assimple linear regression, multiple linear regression, polynomialregression, support vector regression, ridge regression, lassoregression, elasticnet regression, decision tree regression, randomforest regression, logistic regression, logistic classification,K-nearest neighbors, support vector machines, kernel support vectormachines, naive bayes, decision tree classification, random forestclassification, K-means clustering, hierarchical clustering,dimensionality reduction, principal component analysis, lineardiscriminant analysis, kernel principal component analysis, Q-learning,State Action Reward State Action (SARSA), Deep-Q network, Markovdecision processes, Deep Deterministic Policy Gradient (DDPG), or thelike thereof.

In an embodiment, and still referring to FIG. 3 , autonomous machinelearning model may be trained as a function of autonomous training data,wherein autonomous training data may correlate a flight element, pilotsignal, and/or simulation data to an autonomous function. For example,and without limitation, a flight element of an airspeed velocity, apilot signal of limited and/or no control of propulsors, and asimulation data of required airspeed velocity to reach the destinationmay result in an autonomous function that includes a semi-autonomousmode to increase thrust of the propulsors. Autonomous training data maybe received as a function of user-entered valuations of flight elements,pilot signals, simulation data, and/or autonomous functions. Flightcontroller 304 may receive autonomous training data by receivingcorrelations of flight element, pilot signal, and/or simulation data toan autonomous function that were previously received and/or determinedduring a previous iteration of generation of autonomous function.Autonomous training data may be received by one or more remote devicesand/or FPGAs that at least correlate a flight element, pilot signal,and/or simulation data to an autonomous function. Autonomous trainingdata may be received in the form of one or more user-enteredcorrelations of a flight element, pilot signal, and/or simulation datato an autonomous function.

Still referring to FIG. 3 , flight controller 304 may receive autonomousmachine-learning model from a remote device and/or FPGA that utilizesone or more autonomous machine learning processes, wherein a remotedevice and an FPGA is described above in detail. For example, andwithout limitation, a remote device may include a computing device,external device, processor, FPGA, microprocessor and the like thereof.Remote device and/or FPGA may perform the autonomous machine-learningprocess using autonomous training data to generate autonomous functionand transmit the output to flight controller 304. Remote device and/orFPGA may transmit a signal, bit, datum, or parameter to flightcontroller 304 that at least relates to autonomous function.Additionally or alternatively, the remote device and/or FPGA may providean updated machine-learning model. For example, and without limitation,an updated machine-learning model may be comprised of a firmware update,a software update, a autonomous machine-learning process correction, andthe like thereof. As a non-limiting example a software update mayincorporate a new simulation data that relates to a modified flightelement. Additionally or alternatively, the updated machine learningmodel may be transmitted to the remote device and/or FPGA, wherein theremote device and/or FPGA may replace the autonomous machine-learningmodel with the updated machine-learning model and generate theautonomous function as a function of the flight element, pilot signal,and/or simulation data using the updated machine-learning model. Theupdated machine-learning model may be transmitted by the remote deviceand/or FPGA and received by flight controller 304 as a software update,firmware update, or corrected autonomous machine-learning model. Forexample, and without limitation autonomous machine learning model mayutilize a neural net machine-learning process, wherein the updatedmachine-learning model may incorporate a gradient boostingmachine-learning process.

Still referring to FIG. 3 , flight controller 304 may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Further, flight controller may communicate withone or more additional devices as described below in further detail viaa network interface device. The network interface device may be utilizedfor commutatively connecting a flight controller to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. The network may include anynetwork topology and can may employ a wired and/or a wireless mode ofcommunication.

In an embodiment, and still referring to FIG. 3 , flight controller 304may include, but is not limited to, for example, a cluster of flightcontrollers in a first location and a second flight controller orcluster of flight controllers in a second location. Flight controller304 may include one or more flight controllers dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. Flight controller 304 may be configured to distribute one or morecomputing tasks as described below across a plurality of flightcontrollers, which may operate in parallel, in series, redundantly, orin any other manner used for distribution of tasks or memory betweencomputing devices. For example, and without limitation, flightcontroller 304 may implement a control algorithm to distribute and/orcommand the plurality of flight controllers. As used in this disclosurea “control algorithm” is a finite sequence of well-defined computerimplementable instructions that may determine the flight component ofthe plurality of flight components to be adjusted. For example, andwithout limitation, control algorithm may include one or more algorithmsthat reduce and/or prevent aviation asymmetry. As a further non-limitingexample, control algorithms may include one or more models generated asa function of a software including, but not limited to Simulink byMathWorks, Natick, Massachusetts, USA. In an embodiment, and withoutlimitation, control algorithm may be configured to generate anauto-code, wherein an “auto-code,” is used herein, is a code and/oralgorithm that is generated as a function of the one or more modelsand/or software’s. In another embodiment, control algorithm may beconfigured to produce a segmented control algorithm. As used in thisdisclosure a “segmented control algorithm” is control algorithm that hasbeen separated and/or parsed into discrete sections. For example, andwithout limitation, segmented control algorithm may parse controlalgorithm into two or more segments, wherein each segment of controlalgorithm may be performed by one or more flight controllers operatingon distinct flight components.

In an embodiment, and still referring to FIG. 3 , control algorithm maybe configured to determine a segmentation boundary as a function ofsegmented control algorithm. As used in this disclosure a “segmentationboundary” is a limit and/or delineation associated with the segments ofthe segmented control algorithm. For example, and without limitation,segmentation boundary may denote that a segment in the control algorithmhas a first starting section and/or a first ending section. As a furthernon-limiting example, segmentation boundary may include one or moreboundaries associated with an ability of flight component 332. In anembodiment, control algorithm may be configured to create an optimizedsignal communication as a function of segmentation boundary. Forexample, and without limitation, optimized signal communication mayinclude identifying the discrete timing required to transmit and/orreceive the one or more segmentation boundaries. In an embodiment, andwithout limitation, creating optimized signal communication furthercomprises separating a plurality of signal codes across the plurality offlight controllers. For example, and without limitation the plurality offlight controllers may include one or more formal networks, whereinformal networks transmit data along an authority chain and/or arelimited to task-related communications. As a further non-limitingexample, communication network may include informal networks, whereininformal networks transmit data in any direction. In an embodiment, andwithout limitation, the plurality of flight controllers may include achain path, wherein a “chain path,” as used herein, is a linearcommunication path comprising a hierarchy that data may flow through. Inan embodiment, and without limitation, the plurality of flightcontrollers may include an all-channel path, wherein an “all-channelpath,” as used herein, is a communication path that is not restricted toa particular direction. For example, and without limitation, data may betransmitted upward, downward, laterally, and the like thereof. In anembodiment, and without limitation, the plurality of flight controllersmay include one or more neural networks that assign a weighted value toa transmitted datum. For example, and without limitation, a weightedvalue may be assigned as a function of one or more signals denoting thata flight component is malfunctioning and/or in a failure state.

Still referring to FIG. 3 , the plurality of flight controllers mayinclude a master bus controller. As used in this disclosure a “masterbus controller” is one or more devices and/or components that areconnected to a bus to initiate a direct memory access transaction,wherein a bus is one or more terminals in a bus architecture. Master buscontroller may communicate using synchronous and/or asynchronous buscontrol protocols. In an embodiment, master bus controller may includeflight controller 304. In another embodiment, master bus controller mayinclude one or more universal asynchronous receiver-transmitters (UART).For example, and without limitation, master bus controller may includeone or more bus architectures that allow a bus to initiate a directmemory access transaction from one or more buses in the busarchitectures. As a further non-limiting example, master bus controllermay include one or more peripheral devices and/or components tocommunicate with another peripheral device and/or component and/or themaster bus controller. In an embodiment, master bus controller may beconfigured to perform bus arbitration. As used in this disclosure “busarbitration” is method and/or scheme to prevent multiple buses fromattempting to communicate with and/or connect to master bus controller.For example and without limitation, bus arbitration may include one ormore schemes such as a small computer interface system, wherein a smallcomputer interface system is a set of standards for physical connectingand transferring data between peripheral devices and master buscontroller by defining commands, protocols, electrical, optical, and/orlogical interfaces. In an embodiment, master bus controller may receiveintermediate representation 312 and/or output language from logiccomponent 320, wherein output language may include one or moreanalog-to-digital conversions, low bit rate transmissions, messageencryptions, digital signals, binary signals, logic signals, analogsignals, and the like thereof described above in detail.

Still referring to FIG. 3 , master bus controller may communicate with aslave bus. As used in this disclosure a “slave bus” is one or moreperipheral devices and/or components that initiate a bus transfer. Forexample, and without limitation, slave bus may receive one or morecontrols and/or asymmetric communications from master bus controller,wherein slave bus transfers data stored to master bus controller. In anembodiment, and without limitation, slave bus may include one or moreinternal buses, such as but not limited to a/an internal data bus,memory bus, system bus, front-side bus, and the like thereof. In anotherembodiment, and without limitation, slave bus may include one or moreexternal buses such as external flight controllers, external computers,remote devices, printers, aircraft computer systems, flight controlsystems, and the like thereof.

In an embodiment, and still referring to FIG. 3 , control algorithm mayoptimize signal communication as a function of determining one or morediscrete timings. For example, and without limitation master buscontroller may synchronize timing of the segmented control algorithm byinjecting high priority timing signals on a bus of the master buscontrol. As used in this disclosure a “high priority timing signal” isinformation denoting that the information is important. For example, andwithout limitation, high priority timing signal may denote that asection of control algorithm is of high priority and should be analyzedand/or transmitted prior to any other sections being analyzed and/ortransmitted. In an embodiment, high priority timing signal may includeone or more priority packets. As used in this disclosure a “prioritypacket” is a formatted unit of data that is communicated between theplurality of flight controllers. For example, and without limitation,priority packet may denote that a section of control algorithm should beused and/or is of greater priority than other sections.

Still referring to FIG. 3 , flight controller 304 may also beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofaircraft and/or computing device. Flight controller 304 may include adistributer flight controller. As used in this disclosure a “distributerflight controller” is a component that adjusts and/or controls aplurality of flight components as a function of a plurality of flightcontrollers. For example, distributer flight controller may include aflight controller that communicates with a plurality of additionalflight controllers and/or clusters of flight controllers. In anembodiment, distributed flight control may include one or more neuralnetworks. For example, neural network also known as an artificial neuralnetwork, is a network of “nodes,” or data structures having one or moreinputs, one or more outputs, and a function determining outputs based oninputs. Such nodes may be organized in a network, such as withoutlimitation a convolutional neural network, including an input layer ofnodes, one or more intermediate layers, and an output layer of nodes.Connections between nodes may be created via the process of “training”the network, in which elements from a training dataset are applied tothe input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning.

Still referring to FIG. 3 , a node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function (φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above. In anembodiment, and without limitation, a neural network may receivesemantic units as inputs and output vectors representing such semanticunits according to weights w_(i) that are derived using machine-learningprocesses as described in this disclosure.

Still referring to FIG. 3 , flight controller may include asub-controller 340. As used in this disclosure a “sub-controller” is acontroller and/or component that is part of a distributed controller asdescribed above; for instance, flight controller 304 may be and/orinclude a distributed flight controller made up of one or moresub-controllers. For example, and without limitation, sub-controller 340may include any controllers and/or components thereof that are similarto distributed flight controller and/or flight controller as describedabove. Sub-controller 340 may include any component of any flightcontroller as described above. Sub-controller 340 may be implemented inany manner suitable for implementation of a flight controller asdescribed above. As a further non-limiting example, sub-controller 340may include one or more processors, logic components and/or computingdevices capable of receiving, processing, and/or transmitting dataacross the distributed flight controller as described above. As afurther non-limiting example, sub-controller 340 may include acontroller that receives a signal from a first flight controller and/orfirst distributed flight controller component and transmits the signalto a plurality of additional sub-controllers and/or flight components.

Still referring to FIG. 3 , flight controller may include aco-controller 344. As used in this disclosure a “co-controller” is acontroller and/or component that joins flight controller 304 ascomponents and/or nodes of a distributer flight controller as describedabove. For example, and without limitation, co-controller 344 mayinclude one or more controllers and/or components that are similar toflight controller 304. As a further non-limiting example, co-controller344 may include any controller and/or component that joins flightcontroller 304 to distributer flight controller. As a furthernon-limiting example, co-controller 344 may include one or moreprocessors, logic components and/or computing devices capable ofreceiving, processing, and/or transmitting data to and/or from flightcontroller 304 to distributed flight control system. Co-controller 344may include any component of any flight controller as described above.Co-controller 344 may be implemented in any manner suitable forimplementation of a flight controller as described above.

In an embodiment, and with continued reference to FIG. 3 , flightcontroller 304 may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, flight controller 304 may be configured to perform a singlestep or sequence repeatedly until a desired or commanded outcome isachieved; repetition of a step or a sequence of steps may be performediteratively and/or recursively using outputs of previous repetitions asinputs to subsequent repetitions, aggregating inputs and/or outputs ofrepetitions to produce an aggregate result, reduction or decrement ofone or more variables such as global variables, and/or division of alarger processing task into a set of iteratively addressed smallerprocessing tasks. Flight controller may perform any step or sequence ofsteps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Referring now to FIG. 4 , a method 400 for fault detection and controlunder one rotor inoperable condition configured for use in an electricaircraft is illustrated in flow diagram form. At step 405, the methodincludes detecting, at an at least a sensor, a torque datum associatedwith at least a propulsor. Torque datum may be consistent with anytorque datum as described herein. At least a sensor may be consistentwith any sensor or grouping of sensors as described herein. Torque datummay indicate a loss of torque associated with at least a propulsor. Atleast a propulsor may be consistent with any propulsor as describedherein. Torque datum may be detected utilizing a least squares method.Least squares method may be consistent with any least squares method asdescribed herein. Torque loss may be detected if torque datum isdetected at multiple time intervals consistent with torque datums in arange that would indicate torque loss.

Still referring to FIG. 4 , at step 410, method 400 includes generating,at the observer, a torque prediction datum associated with the at leasta propulsor. Observer may be consistent with any observer as describedherein including aircraft motion observer. Torque prediction datum maybe consistent with any torque prediction datum. Observer may compare thetorque prediction datum and the torque datum at regular intervals.Observer may indicate that the torque prediction datum and the torquedatum comparison is greater than a predetermined threshold. Thepredetermined threshold may be consistent with any predeterminedthreshold as described herein.

Still referring to FIG. 4 , at step 415, method 400 includes comparing,at the observer, the torque prediction datum and the torque datum.Observer may be consistent with any observer as described herein. Torqueprediction datum may be consistent with nay torque prediction datum asdescribed herein. Torque datum may be consistent with any torque datumas described.

Still referring to FIG. 4 , at step 420, method 400 includes generating,at the observer, as a function of the comparison, a residual datum.Residual datum may be consistent with any residual datum as describedherein. Residual datum may be a difference between the torque predictiondatum and the torque datum.

Still referring to FIG. 4 , at step 425, method 400 includes generating,at the mixer, as a function of the residual datum, a torque prioritycommand datum. Torque priority command datum may be consistent with anytorque priority command datum. Mixer may include a first mode configuredfor control of a first plurality of propulsors and a second modeconfigured for control of a second plurality of propulsors. Torquepriority command datum comprises increasing torque to the at least apropulsor as a function of the detection of a loss of torque. Loss oftorque may be consistent with any loss of torque as described herein.

Still referring to FIG. 4 , at step 430, method 400 includestransmitting, to the at least a propulsor, the torque priority commanddatum. At least a propulsor may be consistent with any propulsor asdescribed.

Referring now to FIG. 5 , an embodiment of an electric aircraft 500 ispresented. Still referring to FIG. 5 , electric aircraft 500 may includea vertical takeoff and landing aircraft (eVTOL). As used herein, avertical take-off and landing (eVTOL) aircraft is one that can hover,take off, and land vertically. An eVTOL, as used herein, is anelectrically powered aircraft typically using an energy source, of aplurality of energy sources to power the aircraft. In order to optimizethe power and energy necessary to propel the aircraft. eVTOL may becapable of rotor-based cruising flight, rotor-based takeoff, rotor-basedlanding, fixed-wing cruising flight, airplane-style takeoff,airplane-style landing, and/or any combination thereof. Rotor-basedflight, as described herein, is where the aircraft generated lift andpropulsion by way of one or more powered rotors coupled with an engine,such as a “quad copter,” multi-rotor helicopter, or other vehicle thatmaintains its lift primarily using downward thrusting propulsors.Fixed-wing flight, as described herein, is where the aircraft is capableof flight using wings and/or foils that generate life caused by theaircraft’s forward airspeed and the shape of the wings and/or foils,such as airplane-style flight.

With continued reference to FIG. 5 , a number of aerodynamic forces mayact upon the electric aircraft 500 during flight. Forces acting on anelectric aircraft 500 during flight may include, without limitation,thrust, the forward force produced by the rotating element of theelectric aircraft 500 and acts parallel to the longitudinal axis.Another force acting upon electric aircraft 500 may be, withoutlimitation, drag, which may be defined as a rearward retarding forcewhich is caused by disruption of airflow by any protruding surface ofthe electric aircraft 500 such as, without limitation, the wing, rotor,and fuselage. Drag may oppose thrust and acts rearward parallel to therelative wind. A further force acting upon electric aircraft 500 mayinclude, without limitation, weight, which may include a combined loadof the electric aircraft 500 itself, crew, baggage, and/or fuel. Weightmay pull electric aircraft 500 downward due to the force of gravity. Anadditional force acting on electric aircraft 500 may include, withoutlimitation, lift, which may act to oppose the downward force of weightand may be produced by the dynamic effect of air acting on the airfoiland/or downward thrust from the propulsor of the electric aircraft. Liftgenerated by the airfoil may depend on speed of airflow, density of air,total area of an airfoil and/or segment thereof, and/or an angle ofattack between air and the airfoil. For example, and without limitation,electric aircraft 500 are designed to be as lightweight as possible.Reducing the weight of the aircraft and designing to reduce the numberof components is essential to optimize the weight. To save energy, itmay be useful to reduce weight of components of an electric aircraft500, including without limitation propulsors and/or propulsionassemblies. In an embodiment, the motor may eliminate need for manyexternal structural features that otherwise might be needed to join onecomponent to another component. The motor may also increase energyefficiency by enabling a lower physical propulsor profile, reducing dragand/or wind resistance. This may also increase durability by lesseningthe extent to which drag and/or wind resistance add to forces acting onelectric aircraft 500 and/or propulsors.

Referring still to FIG. 5 , Aircraft may include at least a verticalpropulsor 504 and at least a forward propulsor 508. A forward propulsoris a propulsor that propels the aircraft in a forward direction. Forwardin this context is not an indication of the propulsor position on theaircraft; one or more propulsors mounted on the front, on the wings, atthe rear, etc. A vertical propulsor is a propulsor that propels theaircraft in an upward direction; one of more vertical propulsors may bemounted on the front, on the wings, at the rear, and/or any suitablelocation. A propulsor, as used herein, is a component or device used topropel a craft by exerting force on a fluid medium, which may include agaseous medium such as air or a liquid medium such as water. At least avertical propulsor 504 is a propulsor that generates a substantiallydownward thrust, tending to propel an aircraft in a vertical directionproviding thrust for maneuvers such as without limitation, verticaltake-off, vertical landing, hovering, and/or rotor-based flight such as“quadcopter” or similar styles of flight.

With continued reference to FIG. 5 , at least a forward propulsor 508 asused in this disclosure is a propulsor positioned for propelling anaircraft in a “forward” direction; at least a forward propulsor mayinclude one or more propulsors mounted on the front, on the wings, atthe rear, or a combination of any such positions. At least a forwardpropulsor may propel an aircraft forward for fixed-wing and/or“airplane”-style flight, takeoff, and/or landing, and/or may propel theaircraft forward or backward on the ground. At least a verticalpropulsor 504 and at least a forward propulsor 508 includes a thrustelement. At least a thrust element may include any device or componentthat converts the mechanical energy of a motor, for instance in the formof rotational motion of a shaft, into thrust in a fluid medium. At leasta thrust element may include, without limitation, a device using movingor rotating foils, including without limitation one or more rotors, anairscrew or propeller, a set of airscrews or propellers such ascontrarotating propellers, a moving or flapping wing, or the like. Atleast a thrust element may include without limitation a marine propelleror screw, an impeller, a turbine, a pump-jet, a jet, a paddle orpaddle-based device, or the like. As another non-limiting example, atleast a thrust element may include an eight-bladed pusher propeller,such as an eight-bladed propeller mounted behind the engine to ensurethe drive shaft is in compression. Propulsors may include at least amotor mechanically coupled to the at least a first propulsor as a sourceof thrust. A motor may include without limitation, any electric motor,where an electric motor is a device that converts electrical energy intomechanical energy, for instance by causing a shaft to rotate. At least amotor may be driven by direct current (DC) electric power; for instance,at least a first motor may include a brushed DC at least a first motor,or the like. At least a first motor may be driven by electric powerhaving varying or reversing voltage levels, such as alternating current(AC) power as produced by an alternating current generator and/orinverter, or otherwise varying power, such as produced by a switchingpower source. At least a first motor may include, without limitation,brushless DC electric motors, permanent magnet synchronous at least afirst motor, switched reluctance motors, or induction motors. Inaddition to inverter and/or a switching power source, a circuit drivingat least a first motor may include electronic speed controllers or othercomponents for regulating motor speed, rotation direction, and/ordynamic braking. Persons skilled in the art, upon reviewing the entiretyof this disclosure, will be aware of various devices that may be used asat least a thrust element.

With continued reference to FIG. 5 , during flight, a number of forcesmay act upon the electric aircraft. Forces acting on an aircraft 500during flight may include thrust, the forward force produced by therotating element of the aircraft 500 and acts parallel to thelongitudinal axis. Drag may be defined as a rearward retarding forcewhich is caused by disruption of airflow by any protruding surface ofthe aircraft 500 such as, without limitation, the wing, rotor, andfuselage. Drag may oppose thrust and acts rearward parallel to therelative wind. Another force acting on aircraft 500 may include weight,which may include a combined load of the aircraft 500 itself, crew,baggage and fuel. Weight may pull aircraft 500 downward due to the forceof gravity. An additional force acting on aircraft 500 may include lift,which may act to oppose the downward force of weight and may be producedby the dynamic effect of air acting on the airfoil and/or downwardthrust from at least a propulsor. Lift generated by the airfoil maydepends on speed of airflow, density of air, total area of an airfoiland/or segment thereof, and/or an angle of attack between air and theairfoil.

With continued reference to FIG. 5 , at least a portion of an electricaircraft may include at least a propulsor. A propulsor, as used herein,is a component or device used to propel a craft by exerting force on afluid medium, which may include a gaseous medium such as air or a liquidmedium such as water. In an embodiment, when a propulsor twists andpulls air behind it, it will, at the same time, push an aircraft forwardwith an equal amount of force. The more air pulled behind an aircraft,the greater the force with which the aircraft is pushed forward.Propulsor may include any device or component that consumes electricalpower on demand to propel an electric aircraft in a direction or othervehicle while on ground or in-flight.

With continued reference to FIG. 5 , in an embodiment, at least aportion of the aircraft may include a propulsor, the propulsor mayinclude a propeller, a blade, or any combination of the two. Thefunction of a propeller is to convert rotary motion from an engine orother power source into a swirling slipstream which pushes the propellerforwards or backwards. The propulsor may include a rotating power-drivenhub, to which are attached several radial airfoil-section blades suchthat the whole assembly rotates about a longitudinalaxis.https://en.wikipedia.org/wiki/Blade_pitch The blade pitch of thepropellers may, for example, be fixed, manually variable to a few setpositions, automatically variable (e.g. a “constant-speed” type), or anycombination thereof. In an embodiment, propellers for an aircraft aredesigned to be fixed to their hub at an angle similar to the thread on ascrew makes an angle to the shaft; this angle may be referred to as apitch or pitch angle which will determine the speed of the forwardmovement as the blade rotates.

In an embodiment, and still referring to FIG. 5 , an actuator may bemechanically coupled to a control surface at a first end andmechanically coupled to an aircraft, which may include any aircraft asdescribed in this disclosure at a second end. As used herein, a personof ordinary skill in the art would understand “mechanically coupled” tomean that at least a portion of a device, component, or circuit isconnected to at least a portion of the aircraft via a mechanicalcoupling. Said mechanical coupling can include, for example, rigidcoupling, such as beam coupling, bellows coupling, bushed pin coupling,constant velocity, split-muff coupling, diaphragm coupling, disccoupling, donut coupling, elastic coupling, flexible coupling, fluidcoupling, gear coupling, grid coupling, hirth joints, hydrodynamiccoupling, jaw coupling, magnetic coupling, Oldham coupling, sleevecoupling, tapered shaft lock, twin spring coupling, rag joint coupling,universal joints, or any combination thereof. In an embodiment,mechanical coupling can be used to connect the ends of adjacent partsand/or objects of an electric aircraft. Further, in an embodiment,mechanical coupling can be used to join two pieces of rotating electricaircraft components. Control surfaces may each include any portion of anaircraft that can be moved or adjusted to affect altitude, airspeedvelocity, groundspeed velocity or direction during flight. For example,control surfaces may include a component used to affect the aircrafts’roll and pitch which may comprise one or more ailerons, defined hereinas hinged surfaces which form part of the trailing edge of each wing ina fixed wing aircraft, and which may be moved via mechanical means suchas without limitation servomotors, mechanical linkages, or the like, toname a few. As a further example, control surfaces may include a rudder,which may include, without limitation, a segmented rudder. The ruddermay function, without limitation, to control yaw of an aircraft. Also,control surfaces may include other flight control surfaces such aspropulsors, rotating flight controls, or any other structural featureswhich can adjust the movement of the aircraft.

With continued reference to FIG. 5 , in an embodiment, a propulsor caninclude a thrust element which may be integrated into the propulsor. Thethrust element may include, without limitation, a device using moving orrotating foils, such as one or more rotors, an airscrew or propeller, aset of airscrews or propellers such as contra-rotating propellers, amoving or flapping wing, or the like. Further, a thrust element, forexample, can include without limitation a marine propeller or screw, animpeller, a turbine, a pump-jet, a paddle or paddle-based device, or thelike.

Referring now to FIG. 6 , an exemplary embodiment of a machine-learningmodule 600 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 604 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 608 given data provided as inputs 612;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 6 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 604 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 604 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 604 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 604 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 604 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 604 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data604 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively, or additionally, and continuing to refer to FIG. 6 ,training data 604 may include one or more elements that are notcategorized; that is, training data 604 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 604 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person’s name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 604 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 604 used by machine-learning module 600 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample flight elements and/or pilot signals may be inputs, wherein anoutput may be an autonomous function.

Further referring to FIG. 6 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 616. Training data classifier 616 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 600 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 604. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher’slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 416 may classify elements of training data tosub-categories of flight elements such as torques, forces, thrusts,directions, and the like thereof.

Still referring to FIG. 6 , machine-learning module 600 may beconfigured to perform a lazy-learning process 620 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 604. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 604 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naive Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively, or additionally, and with continued reference to FIG. 6 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 624. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 624 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 624 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 604set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 6 , machine-learning algorithms may include atleast a supervised machine-learning process 628. At least a supervisedmachine-learning process 628, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude flight elements and/or pilot signals as described above asinputs, autonomous functions as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 604. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process628 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 6 , machine learning processes may include atleast an unsupervised machine-learning processes 632. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 6 , machine-learning module 600 may be designedand configured to create a machine-learning model 624 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g., a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 6 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Referring now to the drawings, FIG. 7 illustrates an exemplaryembodiment of a dual-motor propulsion assembly 700 of an electricaircraft 704 in accordance with one or more embodiments of the presentdisclosure. In one or more embodiments of the present disclosure,dual-motor propulsion assembly 700 includes a flight component, such aspropulsor 708. As used in this disclosure, a “flight component” is aportion of an electric aircraft that can be used to maneuver and/or movean electric aircraft through a fluid medium, such as a propulsor 708.For the purposes of this disclosure, a “propulsor” is a component ordevice used to propel a craft by exerting force on a fluid medium, whichmay include a gaseous medium such as air or a liquid medium such aswater. Propulsor 708 may include any device or component that consumeselectrical power on demand to propel an electric aircraft in a directionwhile on ground or in-flight. For example, and without limitation,propulsor may include a rotor, propeller, paddle wheel, and the likethereof. In an embodiment, propulsor may include a plurality of bladesthat radially extend from a hub of the propulsor so that the blades mayconvert a rotary motion from a motor into a swirling slipstream. In anembodiment, blade may convert rotary motion to push an aircraft forwardor backward. For instance, and without limitation, propulsor 708 mayinclude an assembly including a rotating power-driven hub, to whichseveral radially-extending airfoil-section blades are fixedly attachedthereto, where the whole assembly rotates about a central longitudinalaxis A. The blade pitch of a propeller may, for example, be fixed,manually variable to a few set positions, automatically variable (e.g.,a “constant-speed” type), or any combination thereof. In an exemplaryembodiment, propellers for an aircraft may be designed to be fixed totheir hub at an angle similar to the thread on a screw makes an angle tothe shaft; this angle may be referred to as a pitch or pitch angle whichwill determine the speed of the forward movement as the blade rotates.In one or more exemplary embodiments, propulsor 708 may include avertical propulsor or a forward propulsor. A forward propulsor mayinclude a propulsor configured to propel aircraft 704 in a forwarddirection. A vertical propulsor may include a propulsor configured topropel aircraft 704 in an upward direction. One of ordinary skill in theart would understand upward to comprise the imaginary axis protrudingfrom the earth at a normal angle, configured to be normal to any tangentplane to a point on a sphere (i.e. skyward). In an embodiment, verticalpropulsor can be a propulsor that generates a substantially downwardthrust, tending to propel an aircraft in an opposite, vertical directionand provides thrust for maneuvers. Such maneuvers can include, withoutlimitation, vertical take-off, vertical landing, hovering, and/orrotor-based flight such as “quadcopter” or similar styles of flight.

In one or more embodiments, propulsor 708 can include a thrust elementwhich may be integrated into the propulsor. The thrust element mayinclude, without limitation, a device using moving or rotating foils,such as one or more rotors, an airscrew, or propeller, a set ofairscrews or propellers such as contra-rotating propellers, a moving orflapping wing, or the like. Further, a thrust element, for example, caninclude without limitation a marine propeller or screw, an impeller, aturbine, a pump-jet, a paddle or paddle-based device, or the like. Inone or more embodiments, propulsor 708 may include a pusher component.As used in this disclosure a “pusher component” is a component thatpushes and/or thrusts an aircraft through a medium. As a non-limitingexample, pusher component may include a pusher propeller, a paddlewheel, a pusher motor, a pusher propulsor, and the like. Pushercomponent may be configured to produce a forward thrust. As used in thisdisclosure a “forward thrust” is a thrust that forces aircraft through amedium in a horizontal direction, wherein a horizontal direction is adirection parallel to the longitudinal axis. For example, forward thrustmay include a force of 1145 N to force electric aircraft 704 in ahorizontal direction along a longitudinal axis of electric aircraft 704.As a further non-limiting example, pusher component may twist and/orrotate to pull air behind it and, at the same time, push electricaircraft 704 forward with an equal amount of force. In an embodiment,and without limitation, the more air forced behind aircraft, the greaterthe thrust force with which electric aircraft 704 is pushed horizontallywill be. In another embodiment, and without limitation, forward thrustmay force electric aircraft 704 through the medium of relative air.Additionally or alternatively, plurality of propulsor may include one ormore puller components. As used in this disclosure a “puller component”is a component that pulls and/or tows an aircraft through a medium. As anon-limiting example, puller component may include a flight componentsuch as a puller propeller, a puller motor, a tractor propeller, apuller propulsor, and the like. Additionally, or alternatively, pullercomponent may include a plurality of puller flight components.

In one or more embodiments, dual-motor propulsion assembly 700 includesa plurality of motors, which includes a first motor 712 and a secondmotor 716 (also referred to herein in the singular as “motor” or pluralas “motors”). Each motor 712,716 is mechanically connected to a flightcomponent, such as propulsor 708, of electric aircraft 704. Motors712,716 are each configured to convert an electrical energy and/orsignal into a mechanical movement of a flight component, such as, forexample, by rotating a shaft attached to propulsor 708 that subsequentlyrotates propulsor 708 about a longitudinal axis A of shaft. In one ormore embodiments, motors 712,716 may be driven by direct current (DC)electric power. For instance, and without limitation, a motor mayinclude a brushed DC motor or the like. In one or more embodiments,motors 712,716 may be a brushless DC electric motor, a permanent magnetsynchronous motor, a switched reluctance motor, and/or an inductionmotor. In other embodiments, motors 712,716 may be driven by electricpower having varying or reversing voltage levels, such as alternatingcurrent (AC) power as produced by an alternating current generatorand/or inverter, or otherwise varying power, such as produced by aswitching power source. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various alternative oradditional forms and/or configurations that a motor may take orexemplify as consistent with this disclosure. In addition to inverterand/or switching power source, a circuit driving motor may includeelectronic speed controllers (not shown) or other components forregulating motor speed, rotation direction, torque, and/or dynamicbraking.

In one or more embodiments, each motor 712,716 may include a rotorcoaxial disposed within a stator. As understood a rotor is a portion ofan electric motor that rotates with respect to a stator, which remainsstationary relative to a corresponding electric aircraft. In one or moreembodiments, assembly 700 includes a shaft that extends through eachmotor 712,716. Motors 712,716 may be arranged such that one motor may bestacked atop the other motor. For example, and without limitation, firstmotor 712 and second motor 716 may share an axis, such as, for example,motors 712,716 may be coaxially positioned along longitudinal axis A ofshaft 720 while first motor 712 may be positioned closer to a flightcomponent than second motor 716 along longitudinal axis A. In one ormore embodiments, assembly 700 may include a clutch. For example, andwithout limitation, each motor 712,716 may include a clutch 724,728,respectively, that engages or disengages shaft 720 upon receipt of ancommand from a controller, as discussed further in this disclosure. Eachclutch 724,728 may include an electro-mechanical clutch. In one or moreembodiments, clutches 724, 728 are configured to engage or disengage apower transmission to each motor 712,716, respectively. In one or moreembodiments, a clutch may include a sprag clutch, electromagneticclutch, a sacrificial weak component to break at a threshold torque,one-time breakaway clutch, such as a sheering element that would breakfree at a designated torque, and/or any other clutch component.

Still referring to FIG. 7 , assembly 700 includes a sensor configured todetect a motor characteristic of motors 712,716. In one or moreembodiments, a sensor may include a first sensor 732 communicativelyconnected to first motor 712, and a second sensor 736 communicativelyconnected to second motor 716. As used in this disclosure, a “sensor” isa device that is configured to detect an event and/or a phenomenon andtransmit information and/or datum related to the detection. Forinstance, and without limitation, a sensor may transform an electricaland/or physical stimulation into an electrical signal that is suitableto be processed by an electrical circuit, such as controller 740. Asensor may generate a sensor output signal, which transmits informationand/or datum related to a detection by the sensor. A sensor outputsignal may include any signal form described in this disclosure, forexample digital, analog, optical, electrical, fluidic, and the like. Insome cases, a sensor, a circuit, and/or a controller may perform one ormore signal processing steps on a signal. For instance, a sensor,circuit, and/or controller may analyze, modify, and/or synthesize asignal in order to improve the signal, for instance by improvingtransmission, storage efficiency, or signal to noise ratio.

In one or more embodiments, each motor 712,716 may include or beconnected to one or more sensors detecting one or more conditions and/orcharacteristics of motors 712,716. One or more conditions may include,without limitation, voltage levels, electromotive force, current levels,temperature, current speed of rotation, position sensors, torque, andthe like. For instance, and without limitation, one or more sensors maybe used to detect torque, or to detect parameters used to determinetorque, as described in further detail below. One or more sensors mayinclude a plurality of current sensors, voltage sensors, speed orposition feedback sensors, such as encoders, and the like. A sensor maycommunicate a current status of motor to a person operating system or acomputing device; computing device may include any computing device asdescribed below, including without limitation, a controller, aprocessor, a microprocessor, a control circuit, a flight controller, orthe like. In one or more embodiments, computing device may use sensorfeedback to calculate performance parameters of motor, including withoutlimitation a torque versus speed operation envelope. Persons skilled inthe art, upon reviewing the entirety of this disclosure, will be awareof various devices and/or components that may be used as or included ina motor or a circuit operating a motor, as used and described herein.

In one or more embodiments, each sensor 732,736 may detect a motorcharacteristic, such as position, displacement, and/or speed, of acomponent of each motor 712,716, respectively. For the purposes of thisdisclosure, a “motor characteristic” is a physical or electricalphenomenon associated with an operation and/or condition of a motor. Inone or more embodiments, a sensor of assembly 700 may generate a failuredatum as a function of a motor characteristic and transmit the failuredatum to a controller. For example, and without limitation, each sensor732,736 may transmit an output signal that, for example, includesfailure datum to a controller, as discussed further in this disclosure.For the purposes of this disclosure, “failure datum” is an electricalsignal representing information related to a motor characteristic of amotor and/or components thereof that indicates a motor malfunction orfailure, such as inoperativeness of a motor.

In one or more embodiments, each sensor 732,736 may include a pluralityof sensors in the form of individual sensors or a sensor suite workingin tandem or individually. A sensor suite may include a sensor arrayhaving a plurality of independent sensors, where any number of thedescribed sensors may be used to detect any number of physical orelectrical quantities associated with an electric vehicle. For example,sensor suite may include a plurality of sensors where each sensordetects the same physical phenomenon. Independent sensors may includeseparate sensors measuring physical or electrical quantities that may bepowered by and/or in communication with circuits independently, whereeach may signal sensor output to a control circuit such as a usergraphical interface. In a non-limiting example, there may be a pluralityof sensors housed in and/or on electric vehicle and/or componentsthereof, such as battery pack of electric aircraft, measuringtemperature, electrical characteristic such as voltage, amperage,resistance, or impedance, or any other parameters and/or quantities asdescribed in this disclosure. In one or more embodiments, use of aplurality of independent sensors may also result in redundancyconfigured to employ more than one sensor that measures the samephenomenon, those sensors being of the same type, a combination of, oranother type of sensor not disclosed, to detect a specificcharacteristic and/or phenomenon.

In one or more embodiments, each sensor 732,736 may include anelectrical sensor. An electrical sensor may be configured to measure avoltage across a component, electrical current through a component, andresistance of a component. In one or more non-limiting embodiments, anelectrical sensor may include a voltmeter, ammeter, ohmmeter, and thelike. For example, and without limitation, an electrical sensor maymeasure power from a power source of an electric aircraft being providedto a motor.

In one or more embodiments, each sensor 732,736 may include atemperature sensor. In one or more embodiments, a temperature sensor mayinclude thermocouples, thermistors, thermometers, infrared sensors,resistance temperature sensors (RTDs), semiconductor based integratedcircuits (IC), and the like. “Temperature”, for the purposes of thisdisclosure, and as would be appreciated by someone of ordinary skill inthe art, is a measure of the heat energy of a system. Temperature, asmeasured by any number or combinations of sensors present, may bemeasured in Fahrenheit (°F), Celsius (°C), Kelvin (°K), or another scalealone, or in combination.

Still referring to FIG. 7 , each sensor 732,736 may include a motionsensor. A motion sensor refers to a device or component configured todetect physical movement of an object or grouping of objects. One ofordinary skill in the art would appreciate, after reviewing the entiretyof this disclosure, that motion may include a plurality of typesincluding but not limited to: spinning, rotating, oscillating, gyrating,jumping, sliding, reciprocating, or the like. A motion sensor mayinclude, torque sensor, gyroscope, accelerometer, position, sensor,magnetometer, inertial measurement unit (IMU), pressure sensor, forcesensor, proximity sensor, displacement sensor, vibration sensor, or thelike.

In one or more embodiments, each sensor 732,736 may include variousother types of sensors configured to detect a physical phenomenon ofeach motor 712,716, respectively. For instance, each sensor 732,736 mayinclude photoelectric sensors, radiation sensors, infrared sensors, andthe like. Each sensor 732,736 may include contact sensors, non-contactsensors, or a combination thereof. In one or more embodiments, eachsensor 732,736 may include digital sensors, analog sensors, or acombination thereof. Each sensor 732,736 may include digital-to-analogconverters (DAC), analog-to-digital converters (ADC, A/D, A-to-D), acombination thereof, or other signal conditioning components used intransmission of measurement data to a destination, such as controller740, over a wireless and/or wired connection.

In one or more embodiments, each sensor 732,736 may include an encoder.In one or more embodiments, first motor 712 may include a first encoder744, and second motor 716 may include a second encoder 748. In one ormore embodiments, each encoder 744,748 may be configured to detect arotation angle of a motor, where the encoder converts an angularposition and/or motion of a shaft of each motor 712,716, respectively,to an analog and/or digital output signal. In some cases, for example,each encoder 744,748 may include a rotational encoder and/or rotaryencoder that is configured to sense a rotational position of a pilotcontrol, such as a throttle level, and/or motor component; in this case,the rotational encoder digitally may sense rotational “clicks” by anyknown method, such as without limitation magnetically, optically, andthe like. In one or more embodiments, encoders 744,748 may include amechanical encoder, optical encoder, on-axis magnetic encoder, and/or anoff-axis magnetic encoder. In one or more embodiments, an encoderincludes an absolute encoder or an incremental encoder. For example, andwithout limitation, encoders 744,748 may include an absolute encoder,which continues to monitor position information related to correspondingmotors 712,716 even when encoders 744,748 are no longer receiving powerfrom, for example, a power source of electric aircraft 704. Once poweris returned to encoders 744,748, encoders 744,748 may provide thedetected position information to a controller. In another example, andwithout limitation, encoder 744,748 may include an incremental encoder,where changes in position of motor are monitored and immediatelyreported by the encoders 744,748. In one or more embodiments, encoders744,748 may include a closed feedback loop or an open feedback loop. Inone or more exemplary embodiments, an encoder is configured to determinea motion of a motor, such as a speed in revolutions per minute of themotor. An encoder may be configured to transmit an output signal, whichincludes feedback, to a controller and/or motor; as a result, the motormay operate based on the received feedback from the encoder. Forexample, and without limitation, a clutch of a motor may engage a shaftof assembly 700 if the motor is determined to be operational based onfeedback from a corresponding encoder. However, if a motor is determinedto be inoperative based on feedback from a corresponding encoder, then aclutch of the motor may be disengaged form the shaft so that the othermotor may engage the shaft and provide motive power to the flightcomponent attached to the shaft.

Still referring to FIG. 7 , dual-motor propulsion assembly 700 includesa controller 740. In one or more embodiments, controller 740 iscommunicatively connected to the plurality of motors. In one or moreembodiments, controller 740 is communicatively connected to each motor712,716. In one or more embodiments, controller 740 may becommunicatively connected to a sensor. For instance, and withoutlimitation, controller 740 may be communicatively connected to eachsensor 732,736. In other embodiments, controller 740 may becommunicatively connected to each clutch 724,728. For the purposes ofthis disclosure, “communicatively connected” is a process whereby onedevice, component, or circuit is able to receive data from and/ortransmit data to another device, component, or circuit. A communicativeconnection may be performed by wired or wireless electroniccommunication; either directly or by way of one or more interveningdevices or components. In an embodiment, a communicative connectionincludes electrically connecting an output of one device, component, orcircuit to an input of another device, component, or circuit.Communicative connection may be performed via a bus or other facilityfor intercommunication between elements of a computing device.Communicative connection may include indirect connections via “wireless”connection, low power wide area network, radio communication, opticalcommunication, magnetic, capacitive, or optical coupling, or the like.In one or more embodiments, a communicative connection may be wirelessand/or wired. For example, controller 740 may communicative with sensors732,736 and/or clutches via a controller area network (CAN)communication.

In one or more embodiments, controller 740 may include a flightcontroller (shown in FIG. 4 ), computing device (shown in FIG. 5 ), acentral processing unit (CPU), a microprocessor, a digital signalprocessor (DSP), an application specific integrated circuit (ASIC), acontrol circuit, a combination thereof, or the like. In one or moreembodiments, output signals, such as motor datum, from sensors 732,736and/or controller 740 may be analog or digital. Controller 740 mayconvert output signals from a sensor to a usable form by the destinationof those signals. The usable form of output signals from sensors 732,736and through controller 740 may be either digital, analog, a combinationthereof, or an otherwise unstated form. Processing by controller 740 maybe configured to trim, offset, or otherwise compensate the outputs ofsensors. Based on output of the sensors, controller 740 may determinethe output to send to a downstream component. Controller 740 may performsignal amplification, operational amplifier (Op-Amp), filter,digital/analog conversion, linearization circuit, current-voltage changecircuits, resistance change circuits such as Wheatstone Bridge, an errorcompensator circuit, a combination thereof or otherwise undisclosedcomponents.

In one or more embodiments, controller 740 may include a timer thatworks in conjunction to determine regular intervals. In otherembodiments, controller 740 may continuously update datum provided bysensors 732,736. Furthermore, data from sensors 732,736 may becontinuously stored on a memory component of controller 740. A timer mayinclude a timing circuit, internal clock, or other circuit, component,or part configured to keep track of elapsed time and/or time of day. Forexample, in non-limiting embodiments, a memory component may save acritical event datum and/or condition datum from sensors 732,736, suchas failure datum, every 30 seconds, every minute, every 30 minutes, oranother time period according to a timer.

Controller 740 may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, controller 740 may be configured to perform a single step orsequence repeatedly until a desired or commanded outcome is achieved.Repetition of a step or a sequence of steps may be performed iterativelyand/or recursively using outputs of previous repetitions as inputs tosubsequent repetitions, aggregating inputs and/or outputs of repetitionsto produce an aggregate result, reduction or decrement of one or morevariables such as global variables, and/or division of a largerprocessing task into a set of iteratively addressed smaller processingtasks. Controller 740 may perform any step or sequence of steps asdescribed in this disclosure in parallel, such as simultaneously and/orsubstantially simultaneously performing a step two or more times usingtwo or more parallel threads, processor cores, or the like; division oftasks between parallel threads and/or processes may be performedaccording to any protocol suitable for division of tasks betweeniterations. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which steps, sequencesof steps, processing tasks, and/or data may be subdivided, shared, orotherwise dealt with using iteration, recursion, and/or parallelprocessing. Controller 740, as well as any other components orcombination of components, may be connected to a controller area network(CAN), which may interconnect all components for signal transmission andreception.

In one or more embodiments, controller 740 may receive a transmittedoutput signal, such as failure datum, from sensors 732,736. For example,and without limitation, first sensor 732 may detect that first motor 712has received a pilot command from a pilot via a pilot control ofelectric aircraft 704, such as a throttle actuation indicating a desiredmotor speed increase. First sensor 732 may then detect a motorcharacteristic of first motor 712. Subsequently, first encoder 744 maytransmit failure datum to controller 740 if first sensor 732 detectsthat motor is inoperative, such as for example, if first motor 712 doesnot move in response to the pilot command. As a result, controller 740may alert, for example, a pilot of the inoperativeness and transmit asignal to second motor to move the flight component. For example, secondmotor may engage shaft 720 and rotate shaft 720 about longitudinal axisA to provide motive power to propulsor 708 so that propulsor moves asintended by the pilot command of the pilot. Therefore, second motor 716provides redundancy such that, if first motor 712 fails, propulsor 708may remain operational as second motor 716 continues to move propulsor708. System redundancies performed by controller 740 and/or motors712,716 may include any systems for redundant flight control asdescribed in U.S. Nonprovisional App. Ser. No. 17/404,614, filed on Aug.17, 2021, and entitled “SYSTEMS AND METHODS FOR REDUNDANT FLIGHT CONTROLIN AN AIRCRAFT,” the entirety of which is incorporated herein byreference. Additional disclosure related to dual motor propulsors may befound in U.S. Pat. App. No. 17/702,069 titled “DUAL-MOTOR PROPULSIONASSEMBLY,” filed on Mar. 23, 2022, and incorporated herein by referencein its entirety.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random-access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 8 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 800 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 800 includes a processor 804 and a memory808 that communicate with each other, and with other components, via abus 812. Bus 812 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 804 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 804 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 804 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating-pointunit (FPU), and/or system on a chip (SoC).

Memory 808 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 816 (BIOS), including basic routines that help totransfer information between elements within computer system 800, suchas during start-up, may be stored in memory 808. Memory 808 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 820 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 808 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 800 may also include a storage device 824. Examples of astorage device (e.g., storage device 824) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 824 may be connected to bus 812 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 824 (or one or morecomponents thereof) may be removably interfaced with computer system 800(e.g., via an external port connector (not shown)). Particularly,storage device 824 and an associated machine-readable medium 828 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 800. In one example, software 820 may reside, completelyor partially, within machine-readable medium 828. In another example,software 820 may reside, completely or partially, within processor 804.

Computer system 800 may also include an input device 832. In oneexample, a user of computer system 800 may enter commands and/or otherinformation into computer system 800 via input device 832. Examples ofan input device 832 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 832may be interfaced to bus 812 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 812, and any combinations thereof. Input device 832 mayinclude a touch screen interface that may be a part of or separate fromdisplay 836, discussed further below. Input device 832 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 800 via storage device 824 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 840. A network interfacedevice, such as network interface device 840, may be utilized forconnecting computer system 800 to one or more of a variety of networks,such as network 844, and one or more remote devices 848 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 844,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 820,etc.) may be communicated to and/or from computer system 800 via networkinterface device 840.

Computer system 800 may further include a video display adapter 852 forcommunicating a displayable image to a display device, such as displaydevice 836. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 852 and display device 836 may be utilized incombination with processor 804 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 800 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 812 via a peripheral interface 856. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for fault detection and control in anelectric aircraft, the system comprising: at least a sensor, wherein theat least a sensor configured to detect a sensed datum associated with atleast a flight component; and a flight controller, the flight controllerconfigured to determine a failure state of the at least a flightcomponent as a function of the sensed datum, wherein the flightcontroller further comprises: a mixer configured to operate in a firstmode in which the mixer is configured to control a plurality ofpropulsors with a first control allocation and a second mode in whichthe mixer is configured to control the plurality of propulsors with asecond control allocation, the mixer comprising circuitry configured to:generate, as a function of the failure state, a torque priority commanddatum; transmit, to the at least a propulsor, the torque prioritycommand datum configured to command operation of at least one propulsorof the plurality propulsors; and wherein the second control allocationhas less attitude control in at least one axis than the first controlallocation.
 2. The system of claim 1, wherein the flight componentcomprises a failed propulsor of the plurality of propulsors and thesensed datum is a function of the failed propulsor of the plurality ofpropulsors.
 3. The system of claim 2, wherein the sensed datum is afunction of a first motor of a plurality of motors of the failedpropulsor.
 4. The system of claim 2, wherein the sensed datum is afunction of a first inverter of a plurality of inverters of the failedpropulsor.
 5. The system of claim 1, wherein the flight componentcomprises a failed propulsor of the plurality of propulsors and thesensed datum is a function of at least a motor of the failed propulsor.6. The system of claim 1, wherein the flight component comprises afailed battery and the sensed datum is a function of the failed battery.7. The system of claim 1, wherein the second control allocation has noyaw control.
 8. The system of claim 1, wherein the torque prioritycommand datum comprises a command to modulate torque to the at least apropulsor as a function of the failure state.
 9. The system of claim 8,wherein determination of the failure state further comprises detectingthe sensed datum associated with the at least a flight component atmultiple time intervals.
 10. The system of claim 1, further comprising:a pilot control located within the electric aircraft and configured togenerate a command as a function of a pilot input; and wherein the mixeris further configured to: receive the command from the pilot control;and generate the torque priority command datum as a function of thecommand.
 11. A method for fault detection and control in an electricaircraft, the method comprising: detecting, at an at least a sensor, asensed datum associated with at least a flight component; determining,at a flight controller, as a function of the sensed datum, a failurestate of the at least a flight component; generating, at a mixer that isconfigured to operate in a first mode in which the mixer is configuredto control a plurality of propulsors with a first control allocation anda second mode in which the mixer is configured to control the pluralitypropulsors with a second control allocation, as a function of thefailure state, a torque priority command datum; and transmitting, to theat least a propulsor, the torque priority command datum configured tocommand operation of at least one propulsor of the plurality ofpropulsors of the electric aircraft; and wherein the second controlallocation has less attitude control in at least one axis than the firstcontrol allocation.
 12. The method of claim 11, wherein the flightcomponent comprises a failed propulsor of the plurality of propulsorsand the sensed datum is a function of the failed propulsor of theplurality of propulsors.
 13. The method of claim 12, wherein the senseddatum is a function of a first motor of a plurality of motors of thefailed propulsor.
 14. The method of claim 12, wherein the sensed datumis a function of a first inverter of a plurality of inverters of thefailed propulsor.
 15. The method of claim 11, wherein the flightcomponent comprises a failed propulsor of the plurality of propulsorsand the sensed datum is a function of at least a motor of the failedpropulsor.
 16. The method of claim 11, wherein the flight componentcomprises a failed battery and the sensed datum is a function of thefailed battery.
 17. The method of claim 11, wherein the second controlallocation has no yaw control.
 18. The method of claim 11, wherein thetorque priority command datum comprises a command to modulate torque tothe at least a propulsor as a function of the failure state.
 19. Themethod of claim 18, wherein determination of the failure state furthercomprises detecting the sensed datum at multiple time intervals.
 20. Themethod of claim 11, further comprising: generating, using a pilotcontrol located within the electric aircraft, a command as function of apilot input; receiving, using the mixer, the command from the pilotcontrol; and generating, using the mixer, the torque priority commanddatum as a function of the command.